File: graphical-ppcs.html

package info (click to toggle)
r-cran-bayesplot 1.15.0-1
links: PTS, VCS
area: main
in suites: forky, sid
size: 7,260 kB
sloc: sh: 13; makefile: 2
file content (832 lines) | stat: -rw-r--r-- 532,462 bytes
<!DOCTYPE html>

<html>

<head>

<meta charset="utf-8" />
<meta name="generator" content="pandoc" />
<meta http-equiv="X-UA-Compatible" content="IE=EDGE" />

<meta name="viewport" content="width=device-width, initial-scale=1" />

<meta name="author" content="Jonah Gabry" />

<meta name="date" content="2025-12-11" />

<title>Graphical posterior predictive checks using the bayesplot package</title>

<script>// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
// be compatible with the behavior of Pandoc < 2.8).
document.addEventListener('DOMContentLoaded', function(e) {
  var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
  var i, h, a;
  for (i = 0; i < hs.length; i++) {
    h = hs[i];
    if (!/^h[1-6]$/i.test(h.tagName)) continue;  // it should be a header h1-h6
    a = h.attributes;
    while (a.length > 0) h.removeAttribute(a[0].name);
  }
});
</script>

<style type="text/css">
code{white-space: pre-wrap;}
span.smallcaps{font-variant: small-caps;}
span.underline{text-decoration: underline;}
div.column{display: inline-block; vertical-align: top; width: 50%;}
div.hanging-indent{margin-left: 1.5em; text-indent: -1.5em;}
ul.task-list{list-style: none;}
</style>



<style type="text/css">
code {
white-space: pre;
}
.sourceCode {
overflow: visible;
}
</style>
<style type="text/css" data-origin="pandoc">
pre > code.sourceCode { white-space: pre; position: relative; }
pre > code.sourceCode > span { line-height: 1.25; }
pre > code.sourceCode > span:empty { height: 1.2em; }
.sourceCode { overflow: visible; }
code.sourceCode > span { color: inherit; text-decoration: inherit; }
div.sourceCode { margin: 1em 0; }
pre.sourceCode { margin: 0; }
@media screen {
div.sourceCode { overflow: auto; }
}
@media print {
pre > code.sourceCode { white-space: pre-wrap; }
pre > code.sourceCode > span { display: inline-block; text-indent: -5em; padding-left: 5em; }
}
pre.numberSource code
{ counter-reset: source-line 0; }
pre.numberSource code > span
{ position: relative; left: -4em; counter-increment: source-line; }
pre.numberSource code > span > a:first-child::before
{ content: counter(source-line);
position: relative; left: -1em; text-align: right; vertical-align: baseline;
border: none; display: inline-block;
-webkit-touch-callout: none; -webkit-user-select: none;
-khtml-user-select: none; -moz-user-select: none;
-ms-user-select: none; user-select: none;
padding: 0 4px; width: 4em;
color: #aaaaaa;
}
pre.numberSource { margin-left: 3em; border-left: 1px solid #aaaaaa; padding-left: 4px; }
div.sourceCode
{ }
@media screen {
pre > code.sourceCode > span > a:first-child::before { text-decoration: underline; }
}
code span.al { color: #ff0000; font-weight: bold; } 
code span.an { color: #60a0b0; font-weight: bold; font-style: italic; } 
code span.at { color: #7d9029; } 
code span.bn { color: #40a070; } 
code span.bu { color: #008000; } 
code span.cf { color: #007020; font-weight: bold; } 
code span.ch { color: #4070a0; } 
code span.cn { color: #880000; } 
code span.co { color: #60a0b0; font-style: italic; } 
code span.cv { color: #60a0b0; font-weight: bold; font-style: italic; } 
code span.do { color: #ba2121; font-style: italic; } 
code span.dt { color: #902000; } 
code span.dv { color: #40a070; } 
code span.er { color: #ff0000; font-weight: bold; } 
code span.ex { } 
code span.fl { color: #40a070; } 
code span.fu { color: #06287e; } 
code span.im { color: #008000; font-weight: bold; } 
code span.in { color: #60a0b0; font-weight: bold; font-style: italic; } 
code span.kw { color: #007020; font-weight: bold; } 
code span.op { color: #666666; } 
code span.ot { color: #007020; } 
code span.pp { color: #bc7a00; } 
code span.sc { color: #4070a0; } 
code span.ss { color: #bb6688; } 
code span.st { color: #4070a0; } 
code span.va { color: #19177c; } 
code span.vs { color: #4070a0; } 
code span.wa { color: #60a0b0; font-weight: bold; font-style: italic; } 
</style>
<script>
// apply pandoc div.sourceCode style to pre.sourceCode instead
(function() {
  var sheets = document.styleSheets;
  for (var i = 0; i < sheets.length; i++) {
    if (sheets[i].ownerNode.dataset["origin"] !== "pandoc") continue;
    try { var rules = sheets[i].cssRules; } catch (e) { continue; }
    var j = 0;
    while (j < rules.length) {
      var rule = rules[j];
      // check if there is a div.sourceCode rule
      if (rule.type !== rule.STYLE_RULE || rule.selectorText !== "div.sourceCode") {
        j++;
        continue;
      }
      var style = rule.style.cssText;
      // check if color or background-color is set
      if (rule.style.color === '' && rule.style.backgroundColor === '') {
        j++;
        continue;
      }
      // replace div.sourceCode by a pre.sourceCode rule
      sheets[i].deleteRule(j);
      sheets[i].insertRule('pre.sourceCode{' + style + '}', j);
    }
  }
})();
</script>




<style type="text/css">body {
background-color: #fff;
margin: 1em auto;
max-width: 700px;
overflow: visible;
padding-left: 2em;
padding-right: 2em;
font-family: "Open Sans", "Helvetica Neue", Helvetica, Arial, sans-serif;
font-size: 14px;
line-height: 1.35;
}
#TOC {
clear: both;
margin: 0 0 10px 10px;
padding: 4px;
width: 400px;
border: 1px solid #CCCCCC;
border-radius: 5px;
background-color: #f6f6f6;
font-size: 13px;
line-height: 1.3;
}
#TOC .toctitle {
font-weight: bold;
font-size: 15px;
margin-left: 5px;
}
#TOC ul {
padding-left: 40px;
margin-left: -1.5em;
margin-top: 5px;
margin-bottom: 5px;
}
#TOC ul ul {
margin-left: -2em;
}
#TOC li {
line-height: 16px;
}
table {
margin: 1em auto;
border-width: 1px;
border-color: #DDDDDD;
border-style: outset;
border-collapse: collapse;
}
table th {
border-width: 2px;
padding: 5px;
border-style: inset;
}
table td {
border-width: 1px;
border-style: inset;
line-height: 18px;
padding: 5px 5px;
}
table, table th, table td {
border-left-style: none;
border-right-style: none;
}
table thead, table tr.even {
background-color: #f7f7f7;
}
p {
margin: 0.5em 0;
}
blockquote {
background-color: #f6f6f6;
padding: 0.25em 0.75em;
}
hr {
border-style: solid;
border: none;
border-top: 1px solid #777;
margin: 28px 0;
}
dl {
margin-left: 0;
}
dl dd {
margin-bottom: 13px;
margin-left: 13px;
}
dl dt {
font-weight: bold;
}
ul {
margin-top: 0;
}
ul li {
list-style: circle outside;
}
ul ul {
margin-bottom: 0;
}
pre, code {
background-color: #f7f7f7;
border-radius: 3px;
color: #333;
white-space: pre-wrap; 
}
pre {
border-radius: 3px;
margin: 5px 0px 10px 0px;
padding: 10px;
}
pre:not([class]) {
background-color: #f7f7f7;
}
code {
font-family: Consolas, Monaco, 'Courier New', monospace;
font-size: 85%;
}
p > code, li > code {
padding: 2px 0px;
}
div.figure {
text-align: center;
}
img {
background-color: #FFFFFF;
padding: 2px;
border: 1px solid #DDDDDD;
border-radius: 3px;
border: 1px solid #CCCCCC;
margin: 0 5px;
}
h1 {
margin-top: 0;
font-size: 35px;
line-height: 40px;
}
h2 {
border-bottom: 4px solid #f7f7f7;
padding-top: 10px;
padding-bottom: 2px;
font-size: 145%;
}
h3 {
border-bottom: 2px solid #f7f7f7;
padding-top: 10px;
font-size: 120%;
}
h4 {
border-bottom: 1px solid #f7f7f7;
margin-left: 8px;
font-size: 105%;
}
h5, h6 {
border-bottom: 1px solid #ccc;
font-size: 105%;
}
a {
color: #0033dd;
text-decoration: none;
}
a:hover {
color: #6666ff; }
a:visited {
color: #800080; }
a:visited:hover {
color: #BB00BB; }
a[href^="http:"] {
text-decoration: underline; }
a[href^="https:"] {
text-decoration: underline; }

code > span.kw { color: #555; font-weight: bold; } 
code > span.dt { color: #902000; } 
code > span.dv { color: #40a070; } 
code > span.bn { color: #d14; } 
code > span.fl { color: #d14; } 
code > span.ch { color: #d14; } 
code > span.st { color: #d14; } 
code > span.co { color: #888888; font-style: italic; } 
code > span.ot { color: #007020; } 
code > span.al { color: #ff0000; font-weight: bold; } 
code > span.fu { color: #900; font-weight: bold; } 
code > span.er { color: #a61717; background-color: #e3d2d2; } 
</style>




</head>

<body>




<h1 class="title toc-ignore">Graphical posterior predictive checks using
the bayesplot package</h1>
<h4 class="author">Jonah Gabry</h4>
<h4 class="date">2025-12-11</h4>


<div id="TOC">
<ul>
<li><a href="#introduction" id="toc-introduction">Introduction</a>
<ul>
<li><a href="#graphical-posterior-predictive-checks-ppcs" id="toc-graphical-posterior-predictive-checks-ppcs">Graphical posterior
predictive checks (PPCs)</a></li>
<li><a href="#setup" id="toc-setup">Setup</a></li>
<li><a href="#example-models" id="toc-example-models">Example
models</a></li>
<li><a href="#defining-y-and-yrep" id="toc-defining-y-and-yrep">Defining
<code>y</code> and <code>yrep</code></a></li>
</ul></li>
<li><a href="#histograms-and-density-estimates" id="toc-histograms-and-density-estimates">Histograms and density
estimates</a></li>
<li><a href="#distributions-of-test-statistics" id="toc-distributions-of-test-statistics">Distributions of test
statistics</a></li>
<li><a href="#other-ppcs-and-ppcs-by-group" id="toc-other-ppcs-and-ppcs-by-group">Other PPCs and PPCs by
group</a></li>
<li><a href="#providing-an-interface-to-bayesplot-ppcs-from-another-package" id="toc-providing-an-interface-to-bayesplot-ppcs-from-another-package">Providing
an interface to bayesplot PPCs from another package</a>
<ul>
<li><a href="#defining-a-pp_check-method" id="toc-defining-a-pp_check-method">Defining a <code>pp_check</code>
method</a></li>
<li><a href="#examples-of-pp_check-methods-in-other-packages" id="toc-examples-of-pp_check-methods-in-other-packages">Examples of
<code>pp_check</code> methods in other packages</a></li>
</ul></li>
<li><a href="#references" id="toc-references">References</a></li>
</ul>
</div>

<div id="introduction" class="section level2">
<h2>Introduction</h2>
<p>This vignette focuses on graphical posterior predictive checks (PPC).
Plots of parameter estimates from MCMC draws are covered in the separate
vignette <a href="https://mc-stan.org/bayesplot/articles/plotting-mcmc-draws.html"><em>Plotting
MCMC draws</em></a>, and MCMC diagnostics are covered in the <a href="https://mc-stan.org/bayesplot/articles/visual-mcmc-diagnostics.html"><em>Visual
MCMC diagnostics</em></a> vignette.</p>
<div id="graphical-posterior-predictive-checks-ppcs" class="section level3">
<h3>Graphical posterior predictive checks (PPCs)</h3>
<p>The <strong>bayesplot</strong> package provides various plotting
functions for <em>graphical posterior predictive checking</em>, that is,
creating graphical displays comparing observed data to simulated data
from the posterior predictive distribution (<a href="#gabry2019">Gabry
et al, 2019</a>).</p>
<p>The idea behind posterior predictive checking is simple: if a model
is a good fit then we should be able to use it to generate data that
looks a lot like the data we observed. To generate the data used for
posterior predictive checks (PPCs) we simulate from the <em>posterior
predictive distribution</em>. This is the distribution of the outcome
variable implied by a model after using the observed data <span class="math inline">\(y\)</span> (a vector of <span class="math inline">\(N\)</span> outcome values) to update our beliefs
about unknown model parameters <span class="math inline">\(\theta\)</span>. The posterior predictive
distribution for observation <span class="math inline">\(\widetilde{y}\)</span> can be written as <span class="math display">\[p(\widetilde{y} \,|\, y) = \int
p(\widetilde{y} \,|\, \theta) \, p(\theta \,|\, y) \, d\theta.\]</span>
Typically we will also condition on <span class="math inline">\(X\)</span> (a matrix of predictor variables).</p>
<p>For each draw (simulation) <span class="math inline">\(s = 1, \ldots,
S\)</span> of the parameters from the posterior distribution, <span class="math inline">\(\theta^{(s)} \sim p(\theta \,|\, y)\)</span>, we
draw an entire vector of <span class="math inline">\(N\)</span> outcomes
<span class="math inline">\(\widetilde{y}^{(s)}\)</span> from the
posterior predictive distribution by simulating from the data model
conditional on parameters <span class="math inline">\(\theta^{(s)}\)</span>. The result is an <span class="math inline">\(S \times N\)</span> matrix of draws <span class="math inline">\(\widetilde{y}\)</span>.</p>
<p>When simulating from the posterior predictive distribution we can use
either the same values of the predictors <span class="math inline">\(X\)</span> that we used when fitting the model or
new observations of those predictors. When we use the same values of
<span class="math inline">\(X\)</span> we denote the resulting
simulations by <span class="math inline">\(y^{rep}\)</span>, as they can
be thought of as replications of the outcome <span class="math inline">\(y\)</span> rather than predictions for future
observations (<span class="math inline">\(\widetilde{y}\)</span> using
predictors <span class="math inline">\(\widetilde{X}\)</span>). This
corresponds to the notation from Gelman et al. (2013) and is the
notation used throughout the package documentation.</p>
<p>Using the replicated datasets drawn from the posterior predictive
distribution, the functions in the <strong>bayesplot</strong> package
create various graphical displays comparing the observed data <span class="math inline">\(y\)</span> to the replications. The names of the
<strong>bayesplot</strong> plotting functions for posterior predictive
checking all have the prefix <code>ppc_</code>.</p>
</div>
<div id="setup" class="section level3">
<h3>Setup</h3>
<p>In addition to <strong>bayesplot</strong> we’ll load the following
packages:</p>
<ul>
<li><strong>ggplot2</strong>, in case we want to customize the ggplot
objects created by <strong>bayesplot</strong></li>
<li><strong>rstanarm</strong>, for fitting the example models used
throughout the vignette</li>
</ul>
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" tabindex="-1"></a><span class="fu">library</span>(<span class="st">&quot;bayesplot&quot;</span>)</span>
<span id="cb1-2"><a href="#cb1-2" tabindex="-1"></a><span class="fu">library</span>(<span class="st">&quot;ggplot2&quot;</span>)</span>
<span id="cb1-3"><a href="#cb1-3" tabindex="-1"></a><span class="fu">library</span>(<span class="st">&quot;rstanarm&quot;</span>)</span></code></pre></div>
</div>
<div id="example-models" class="section level3">
<h3>Example models</h3>
<p>To demonstrate some of the various PPCs that can be created with the
<strong>bayesplot</strong> package we’ll use an example of comparing
Poisson and Negative binomial regression models from one of the
<strong>rstanarm</strong> <a href="https://mc-stan.org/rstanarm/articles/count.html">package
vignettes</a> (Gabry and Goodrich, 2017).</p>
<blockquote>
<p>We want to make inferences about the efficacy of a certain pest
management system at reducing the number of roaches in urban apartments.
[…] The regression predictors for the model are the pre-treatment number
of roaches <code>roach1</code>, the treatment indicator
<code>treatment</code>, and a variable <code>senior</code> indicating
whether the apartment is in a building restricted to elderly residents.
Because the number of days for which the roach traps were used is not
the same for all apartments in the sample, we include it as an exposure
[…].</p>
</blockquote>
<p>First we fit a Poisson regression model with outcome variable
<code>y</code> representing the roach count in each apartment at the end
of the experiment.</p>
<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" tabindex="-1"></a><span class="fu">head</span>(roaches) <span class="co"># see help(&quot;rstanarm-datasets&quot;)</span></span></code></pre></div>
<pre><code>    y roach1 treatment senior exposure2
1 153 308.00         1      0  0.800000
2 127 331.25         1      0  0.600000
3   7   1.67         1      0  1.000000
4   7   3.00         1      0  1.000000
5   0   2.00         1      0  1.142857
6   0   0.00         1      0  1.000000</code></pre>
<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1" tabindex="-1"></a>roaches<span class="sc">$</span>roach100 <span class="ot">&lt;-</span> roaches<span class="sc">$</span>roach1 <span class="sc">/</span> <span class="dv">100</span> <span class="co"># pre-treatment number of roaches (in 100s)</span></span></code></pre></div>
<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1" tabindex="-1"></a><span class="co"># using rstanarm&#39;s default priors. For details see the section on default</span></span>
<span id="cb5-2"><a href="#cb5-2" tabindex="-1"></a><span class="co"># weakly informative priors at https://mc-stan.org/rstanarm/articles/priors.html</span></span>
<span id="cb5-3"><a href="#cb5-3" tabindex="-1"></a>fit_poisson <span class="ot">&lt;-</span> <span class="fu">stan_glm</span>(</span>
<span id="cb5-4"><a href="#cb5-4" tabindex="-1"></a>  y <span class="sc">~</span> roach100 <span class="sc">+</span> treatment <span class="sc">+</span> senior,</span>
<span id="cb5-5"><a href="#cb5-5" tabindex="-1"></a>  <span class="at">offset =</span> <span class="fu">log</span>(exposure2),</span>
<span id="cb5-6"><a href="#cb5-6" tabindex="-1"></a>  <span class="at">family =</span> <span class="fu">poisson</span>(<span class="at">link =</span> <span class="st">&quot;log&quot;</span>),</span>
<span id="cb5-7"><a href="#cb5-7" tabindex="-1"></a>  <span class="at">data =</span> roaches,</span>
<span id="cb5-8"><a href="#cb5-8" tabindex="-1"></a>  <span class="at">seed =</span> <span class="dv">1111</span>, </span>
<span id="cb5-9"><a href="#cb5-9" tabindex="-1"></a>  <span class="at">refresh =</span> <span class="dv">0</span> <span class="co"># suppresses all output as of v2.18.1 of rstan</span></span>
<span id="cb5-10"><a href="#cb5-10" tabindex="-1"></a>)</span></code></pre></div>
<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1" tabindex="-1"></a><span class="fu">print</span>(fit_poisson)</span></code></pre></div>
<pre><code>stan_glm
 family:       poisson [log]
 formula:      y ~ roach100 + treatment + senior
 observations: 262
 predictors:   4
------
            Median MAD_SD
(Intercept)  3.1    0.0  
roach100     0.7    0.0  
treatment   -0.5    0.0  
senior      -0.4    0.0  

------
* For help interpreting the printed output see ?print.stanreg
* For info on the priors used see ?prior_summary.stanreg</code></pre>
<p>We’ll also fit the negative binomial model that we’ll compare to the
Poisson:</p>
<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1" tabindex="-1"></a>fit_nb <span class="ot">&lt;-</span> <span class="fu">update</span>(fit_poisson, <span class="at">family =</span> <span class="st">&quot;neg_binomial_2&quot;</span>)</span></code></pre></div>
<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1" tabindex="-1"></a><span class="fu">print</span>(fit_nb)</span></code></pre></div>
<pre><code>stan_glm
 family:       neg_binomial_2 [log]
 formula:      y ~ roach100 + treatment + senior
 observations: 262
 predictors:   4
------
            Median MAD_SD
(Intercept)  2.8    0.2  
roach100     1.3    0.3  
treatment   -0.8    0.2  
senior      -0.3    0.3  

Auxiliary parameter(s):
                      Median MAD_SD
reciprocal_dispersion 0.3    0.0   

------
* For help interpreting the printed output see ?print.stanreg
* For info on the priors used see ?prior_summary.stanreg</code></pre>
</div>
<div id="defining-y-and-yrep" class="section level3">
<h3>Defining <code>y</code> and <code>yrep</code></h3>
<p>In order to use the PPC functions from the <strong>bayesplot</strong>
package we need a vector <code>y</code> of outcome values,</p>
<div class="sourceCode" id="cb11"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb11-1"><a href="#cb11-1" tabindex="-1"></a>y <span class="ot">&lt;-</span> roaches<span class="sc">$</span>y</span></code></pre></div>
<p>and a matrix <code>yrep</code> of draws from the posterior predictive
distribution,</p>
<div class="sourceCode" id="cb12"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb12-1"><a href="#cb12-1" tabindex="-1"></a>yrep_poisson <span class="ot">&lt;-</span> <span class="fu">posterior_predict</span>(fit_poisson, <span class="at">draws =</span> <span class="dv">500</span>)</span>
<span id="cb12-2"><a href="#cb12-2" tabindex="-1"></a>yrep_nb <span class="ot">&lt;-</span> <span class="fu">posterior_predict</span>(fit_nb, <span class="at">draws =</span> <span class="dv">500</span>)</span>
<span id="cb12-3"><a href="#cb12-3" tabindex="-1"></a><span class="fu">dim</span>(yrep_poisson)</span></code></pre></div>
<pre><code>[1] 500 262</code></pre>
<div class="sourceCode" id="cb14"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb14-1"><a href="#cb14-1" tabindex="-1"></a><span class="fu">dim</span>(yrep_nb)</span></code></pre></div>
<pre><code>[1] 500 262</code></pre>
<p>Each row of the matrix is a draw from the posterior predictive
distribution, i.e. a vector with one element for each of the data points
in <code>y</code>.</p>
<p>Since we fit the models using <strong>rstanarm</strong> we used its
special <code>posterior_predict</code> function, but if we were using a
model fit with the <strong>rstan</strong> package we could create
<code>yrep</code> in the <code>generated quantities</code> block of the
Stan program or by doing simulations in R after fitting the model. Draws
from the posterior predictive distribution can be used with
<strong>bayesplot</strong> regardless of whether or not the model was
fit using an interface to Stan. <strong>bayesplot</strong> just requires
a <code>yrep</code> matrix that has <code>number_of_draws</code> rows
and <code>number_of_observations</code> columns.</p>
<p><br></p>
</div>
</div>
<div id="histograms-and-density-estimates" class="section level2">
<h2>Histograms and density estimates</h2>
<div id="ppc_dens_overlay" class="section level4">
<h4>ppc_dens_overlay</h4>
<p>The first PPC we’ll look at is a comparison of the distribution of
<code>y</code> and the distributions of some of the simulated datasets
(rows) in the <code>yrep</code> matrix.</p>
<div class="sourceCode" id="cb16"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb16-1"><a href="#cb16-1" tabindex="-1"></a><span class="fu">color_scheme_set</span>(<span class="st">&quot;brightblue&quot;</span>)</span>
<span id="cb16-2"><a href="#cb16-2" tabindex="-1"></a><span class="fu">ppc_dens_overlay</span>(y, yrep_poisson[<span class="dv">1</span><span class="sc">:</span><span class="dv">50</span>, ])</span></code></pre></div>
<p><img role="img" src="data:image/png;base64,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" width="60%" style="display: block; margin: auto;" /></p>
<p>In the plot above, the dark line is the distribution of the observed
outcomes <code>y</code> and each of the 50 lighter lines is the kernel
density estimate of one of the replications of <code>y</code> from the
posterior predictive distribution (i.e., one of the rows in
<code>yrep</code>). This plot makes it easy to see that this model fails
to account for the large proportion of zeros in <code>y</code>. That is,
the model predicts fewer zeros than were actually observed.</p>
<p>To see the discrepancy at the lower values of more clearly we can use
the <code>xlim</code> function from <strong>ggplot2</strong> to restrict
the range of the x-axis:</p>
<div class="sourceCode" id="cb17"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb17-1"><a href="#cb17-1" tabindex="-1"></a><span class="fu">ppc_dens_overlay</span>(y, yrep_poisson[<span class="dv">1</span><span class="sc">:</span><span class="dv">50</span>, ]) <span class="sc">+</span> <span class="fu">xlim</span>(<span class="dv">0</span>, <span class="dv">150</span>)</span></code></pre></div>
<p><img role="img" src="data:image/png;base64,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" width="60%" style="display: block; margin: auto;" /></p>
<p>See Figure 6 in <a href="#gabry2019">Gabry et al. (2019)</a> for
another example of using <code>ppc_dens_overlay</code>.</p>
</div>
<div id="ppc_hist" class="section level4">
<h4>ppc_hist</h4>
<p>We could see the same thing from a different perspective by looking
at separate histograms of <code>y</code> and some of the
<code>yrep</code> datasets using the <code>ppc_hist</code> function:</p>
<div class="sourceCode" id="cb18"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb18-1"><a href="#cb18-1" tabindex="-1"></a><span class="fu">ppc_hist</span>(y, yrep_poisson[<span class="dv">1</span><span class="sc">:</span><span class="dv">5</span>, ])</span></code></pre></div>
<p><img role="img" src="data:image/png;base64,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" width="60%" style="display: block; margin: auto;" /></p>
<p>The same plot for the negative binomial model looks much
different:</p>
<div class="sourceCode" id="cb19"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb19-1"><a href="#cb19-1" tabindex="-1"></a><span class="fu">ppc_hist</span>(y, yrep_nb[<span class="dv">1</span><span class="sc">:</span><span class="dv">5</span>, ])</span></code></pre></div>
<p><img role="img" src="data:image/png;base64,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" width="60%" style="display: block; margin: auto;" /></p>
<p>The negative binomial model does better handling the number of zeros
in the data, but it occasionally predicts values that are way too large,
which is why the x-axes extend to such high values in the plot and make
it difficult to read. To see the predictions for the smaller values more
clearly we can zoom in:</p>
<div class="sourceCode" id="cb20"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb20-1"><a href="#cb20-1" tabindex="-1"></a><span class="fu">ppc_hist</span>(y, yrep_nb[<span class="dv">1</span><span class="sc">:</span><span class="dv">5</span>, ], <span class="at">binwidth =</span> <span class="dv">20</span>) <span class="sc">+</span> </span>
<span id="cb20-2"><a href="#cb20-2" tabindex="-1"></a>  <span class="fu">coord_cartesian</span>(<span class="at">xlim =</span> <span class="fu">c</span>(<span class="sc">-</span><span class="dv">1</span>, <span class="dv">300</span>))</span></code></pre></div>
<p><img role="img" src="data:image/png;base64,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" width="60%" style="display: block; margin: auto;" /></p>
<p><br></p>
</div>
</div>
<div id="distributions-of-test-statistics" class="section level2">
<h2>Distributions of test statistics</h2>
<p>Another way to see that the Poisson model predicts too few zeros is
to look at the distribution of the proportion of zeros over the
replicated datasets from the posterior predictive distribution in
<code>yrep</code> and compare to the proportion of observed zeros in
<code>y</code>.</p>
<div id="ppc_stat" class="section level4">
<h4>ppc_stat</h4>
<p>First we define a function that takes a vector as input and returns
the proportion of zeros:</p>
<div class="sourceCode" id="cb21"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb21-1"><a href="#cb21-1" tabindex="-1"></a>prop_zero <span class="ot">&lt;-</span> <span class="cf">function</span>(x) <span class="fu">mean</span>(x <span class="sc">==</span> <span class="dv">0</span>)</span>
<span id="cb21-2"><a href="#cb21-2" tabindex="-1"></a><span class="fu">prop_zero</span>(y) <span class="co"># check proportion of zeros in y</span></span></code></pre></div>
<pre><code>[1] 0.3587786</code></pre>
<p>The <code>stat</code> argument to <code>ppc_stat</code> accepts a
function or the name of a function for computing a test statistic from a
vector of data. In our case we can specify
<code>stat = &quot;prop_zero&quot;</code> since we’ve already defined the
<code>prop_zero</code> function, but we also could have used
<code>stat = function(x) mean(x == 0)</code>.</p>
<div class="sourceCode" id="cb23"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb23-1"><a href="#cb23-1" tabindex="-1"></a><span class="fu">ppc_stat</span>(y, yrep_poisson, <span class="at">stat =</span> <span class="st">&quot;prop_zero&quot;</span>, <span class="at">binwidth =</span> <span class="fl">0.005</span>)</span></code></pre></div>
<p><img role="img" src="data:image/png;base64,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" width="60%" style="display: block; margin: auto;" /></p>
<p>The dark line is at the value <span class="math inline">\(T(y)\)</span>, i.e. the value of the test
statistic computed from the observed <span class="math inline">\(y\)</span>, in this case
<code>prop_zero(y)</code>. The lighter area on the left is actually a
histogram of the proportion of zeros in in the <code>yrep</code>
simulations, but it can be hard to see because almost none of the
simulated datasets in <code>yrep</code> have any zeros.</p>
<p>Here’s the same plot for the negative binomial model:</p>
<div class="sourceCode" id="cb24"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb24-1"><a href="#cb24-1" tabindex="-1"></a><span class="fu">ppc_stat</span>(y, yrep_nb, <span class="at">stat =</span> <span class="st">&quot;prop_zero&quot;</span>)</span></code></pre></div>
<p><img role="img" src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAu4AAAHPCAYAAAAMBV/EAAAEDmlDQ1BrQ0dDb2xvclNwYWNlR2VuZXJpY1JHQgAAOI2NVV1oHFUUPpu5syskzoPUpqaSDv41lLRsUtGE2uj+ZbNt3CyTbLRBkMns3Z1pJjPj/KRpKT4UQRDBqOCT4P9bwSchaqvtiy2itFCiBIMo+ND6R6HSFwnruTOzu5O4a73L3PnmnO9+595z7t4LkLgsW5beJQIsGq4t5dPis8fmxMQ6dMF90A190C0rjpUqlSYBG+PCv9rt7yDG3tf2t/f/Z+uuUEcBiN2F2Kw4yiLiZQD+FcWyXYAEQfvICddi+AnEO2ycIOISw7UAVxieD/Cyz5mRMohfRSwoqoz+xNuIB+cj9loEB3Pw2448NaitKSLLRck2q5pOI9O9g/t/tkXda8Tbg0+PszB9FN8DuPaXKnKW4YcQn1Xk3HSIry5ps8UQ/2W5aQnxIwBdu7yFcgrxPsRjVXu8HOh0qao30cArp9SZZxDfg3h1wTzKxu5E/LUxX5wKdX5SnAzmDx4A4OIqLbB69yMesE1pKojLjVdoNsfyiPi45hZmAn3uLWdpOtfQOaVmikEs7ovj8hFWpz7EV6mel0L9Xy23FMYlPYZenAx0yDB1/PX6dledmQjikjkXCxqMJS9WtfFCyH9XtSekEF+2dH+P4tzITduTygGfv58a5VCTH5PtXD7EFZiNyUDBhHnsFTBgE0SQIA9pfFtgo6cKGuhooeilaKH41eDs38Ip+f4At1Rq/sjr6NEwQqb/I/DQqsLvaFUjvAx+eWirddAJZnAj1DFJL0mSg/gcIpPkMBkhoyCSJ8lTZIxk0TpKDjXHliJzZPO50dR5ASNSnzeLvIvod0HG/mdkmOC0z8VKnzcQ2M/Yz2vKldduXjp9bleLu0ZWn7vWc+l0JGcaai10yNrUnXLP/8Jf59ewX+c3Wgz+B34Df+vbVrc16zTMVgp9um9bxEfzPU5kPqUtVWxhs6OiWTVW+gIfywB9uXi7CGcGW/zk98k/kmvJ95IfJn/j3uQ+4c5zn3Kfcd+AyF3gLnJfcl9xH3OfR2rUee80a+6vo7EK5mmXUdyfQlrYLTwoZIU9wsPCZEtP6BWGhAlhL3p2N6sTjRdduwbHsG9kq32sgBepc+xurLPW4T9URpYGJ3ym4+8zA05u44QjST8ZIoVtu3qE7fWmdn5LPdqvgcZz8Ww8BWJ8X3w0PhQ/wnCDGd+LvlHs8dRy6bLLDuKMaZ20tZrqisPJ5ONiCq8yKhYM5cCgKOu66Lsc0aYOtZdo5QCwezI4wm9J/v0X23mlZXOfBjj8Jzv3WrY5D+CsA9D7aMs2gGfjve8ArD6mePZSeCfEYt8CONWDw8FXTxrPqx/r9Vt4biXeANh8vV7/+/16ffMD1N8AuKD/A/8leAvFY9bLAAAAOGVYSWZNTQAqAAAACAABh2kABAAAAAEAAAAaAAAAAAACoAIABAAAAAEAAALuoAMABAAAAAEAAAHPAAAAAE2s/msAAEAASURBVHgB7d0HmGRFuTjub9llE5klR0FAyegFFBARAckIkgTkInB5EL34X/ldCSqIeJWgiD4IKiJiIHkvYiSZECUYAEkCSpawS1oWlrD539/x9tizO7M7c6Z7pvvMW88zdPc5p+pUvdX0fl1dp86IubUUEgECBAgQIECAAAECbS2wSFvXTuUIECBAgAABAgQIECgEBO7eCAQIECBAgAABAgQ6QEDg3gGdpIoECBAgQIAAAQIEBO7eAwQIECBAgAABAgQ6QEDg3gGdpIoECBAgQIAAAQIEBO7eAwQIECBAgAABAgQ6QEDg3gGdpIoECBAgQIAAAQIEBO7eAwQIECBAgAABAgQ6QEDg3gGdpIoECBAgQIAAAQIEBO7eAwQIECBAgAABAgQ6QGBUf+o4a9asmD59eowdOzZGjhzZn6yOJTBsBGbPGXhTFxkRMaL2JxEgQIAAAQIE6gL9CtzPO++8mDhxYlx++eVx4IEH1svwSIBAg8D3bo+YOTti3KING/vxdMqrEW9bI2LL2l+npMeenhrrvu9rPVb3YwdvGWce++4e99lIgAABAgQI9F2gX4F734t1JIHhLTBhsYjlan9l0oxZZXLJQ4AAAQIECFRdwBz3qvew9hEgQIAAAQIECFRCQOBeiW7UCAIECBAgQIAAgaoLCNyr3sPaR4AAAQIECBAgUAkBgXslulEjCBAgQIAAAQIEqi4gcK96D2sfAQIECBAgQIBAJQQE7pXoRo0gQIAAAQIECBCouoDAveo9rH0ECBAgQIAAAQKVEBC4V6IbNYIAAQIECBAgQKDqAgL3qvew9hEgQIAAAQIECFRCQOBeiW7UCAIECBAgQIAAgaoLCNyr3sPaR4AAAQIECBAgUAkBgXslulEjCBAgQIAAAQIEqi4gcK96D2sfAQIECBAgQIBAJQQE7pXoRo0gQIAAAQIECBCouoDAveo9rH0ECBAgQIAAAQKVEBC4V6IbNYIAAQIECBAgQKDqAgL3qvew9hEgQIAAAQIECFRCQOBeiW7UCAIECBAgQIAAgaoLCNyr3sPaR4AAAQIECBAgUAkBgXslulEjCBAgQIAAAQIEqi4gcK96D2sfAQIECBAgQIBAJQQE7pXoRo0gQIAAAQIECBCouoDAveo9rH0ECBAgQIAAAQKVEBC4V6IbNYIAAQIECBAgQKDqAgL3qvew9hEgQIAAAQIECFRCQOBeiW7UCAIECBAgQIAAgaoLCNyr3sPaR4AAAQIECBAgUAkBgXslulEjCBAgQIAAAQIEqi4gcK96D2sfAQIECBAgQIBAJQQE7pXoRo0gQIAAAQIECBCouoDAveo9rH0ECBAgQIAAAQKVEBC4V6IbNYIAAQIECBAgQKDqAgL3qvew9hEgQIAAAQIECFRCQOBeiW7UCAIECBAgQIAAgaoLCNyr3sPaR4AAAQIECBAgUAkBgXslulEjCBAgQIAAAQIEqi4gcK96D2sfAQIECBAgQIBAJQQE7pXoRo0gQIAAAQIECBCouoDAveo9rH0ECBAgQIAAAQKVEBC4V6IbNYIAAQIECBAgQKDqAgL3qvew9hEgQIAAAQIECFRCQOBeiW7UCAIECBAgQIAAgaoLCNyr3sPaR4AAAQIECBAgUAkBgXslulEjCBAgQIAAAQIEqi4gcK96D2sfAQIECBAgQIBAJQQE7pXoRo0gQIAAAQIECBCouoDAveo9rH0ECBAgQIAAAQKVEBC4V6IbNYIAAQIECBAgQKDqAgL3qvew9hEgQIAAAQIECFRCQOBeiW7UCAIECBAgQIAAgaoLCNyr3sPaR4AAAQIECBAgUAkBgXslulEjCBAgQIAAAQIEqi4gcK96D2sfAQIECBAgQIBAJQQE7pXoRo0gQIAAAQIECBCouoDAveo9rH0ECBAgQIAAAQKVEBC4V6IbNYIAAQIECBAgQKDqAgL3qvew9hEgQIAAAQIECFRCQOBeiW7UCAIECBAgQIAAgaoLCNyr3sPaR4AAAQIECBAgUAkBgXslulEjCBAgQIAAAQIEqi4gcK96D2sfAQIECBAgQIBAJQQE7pXoRo0gQIAAAQIECBCouoDAveo9rH0ECBAgQIAAAQKVEBC4V6IbNYIAAQIECBAgQKDqAgL3qvew9hEgQIAAAQIECFRCQOBeiW7UCAIECBAgQIAAgaoLCNyr3sPaR4AAAQIECBAgUAkBgXslulEjCBAgQIAAAQIEqi4gcK96D2sfAQIECBAgQIBAJQQE7pXoRo0gQIAAAQIECBCouoDAveo9rH0ECBAgQIAAAQKVEBC4V6IbNYIAAQIECBAgQKDqAgL3qvew9hEgQIAAAQIECFRCQOBeiW7UCAIECBAgQIAAgaoLCNyr3sPaR4AAAQIECBAgUAkBgXslulEjCBAgQIAAAQIEqi4gcK96D2sfAQIECBAgQIBAJQQE7pXoRo0gQIAAAQIECBCouoDAveo9rH0ECBAgQIAAAQKVEBC4V6IbNYIAAQIECBAgQKDqAqOq3kDtI0CgfwIvvR4x5bX+5Xlyav+OdzQBAgQIECDQfwGBe//N5CBQaYH7n4m48ZGIMSP73swXpvT9WEcSIECAAAEC5QQE7uXc5CJQaYFlx0Wss3zfmzh5dN+PdSQBAgQIECBQTsAc93JuchEgQIAAAQIECBAYVAGB+6ByOxkBAgQIECBAgACBcgIC93JuchEgQIAAAQIECBAYVAGB+6ByOxkBAgQIECBAgACBcgIC93JuchEgQIAAAQIECBAYVAGB+6ByOxkBAgQIECBAgACBcgIC93JuchEgQIAAAQIECBAYVAGB+6ByOxkBAgQIECBAgACBcgIC93JuchEgQIAAAQIECBAYVAGB+6ByOxkBAgQIECBAgACBcgIC93JuchEgQIAAAQIECBAYVAGB+6ByOxkBAgQIECBAgACBcgIC93JuchEgQIAAAQIECBAYVAGB+6ByOxkBAgQIECBAgACBcgIC93JuchEgQIAAAQIECBAYVAGB+6ByOxkBAgQIECBAgACBcgIC93JuchEgQIAAAQIECBAYVAGB+6ByOxkBAgQIECBAgACBcgKjymWTiwCBVgnMmBXxxNSIUU+UP8MKi0estnT5/HISIECAAAEC7ScgcG+/PlGjYS4wffY/A/cpr5WDmDY9YuOVBe7l9OQiQIAAAQLtKyBwb9++UbNhLJAj5qssVQ7g0RfK5ZOLAAECBAgQaG8Bc9zbu3/UjgABAgQIECBAgEAhIHD3RiBAgAABAgQIECDQAQIC9w7oJFUkQIAAAQIECBAgIHD3HiBAgAABAgQIECDQAQIuTu2ATlLF/gnc+ljE3P5lme/oDVeMWHLsfJttIECAAAECBAgMmYDAfcjonbhVAjc+HLHs+IiRJX9Pev6ViNVrK7oI3FvVQ8olQIAAAQIEyggI3MuoydP2AmssEzF20XLVfLm2DrpEgAABAgQIEGg3gZJjku3WDPUhQIAAAQIECBAgUG0BgXu1+1frCBAgQIAAAQIEKiIgcK9IR2oGAQIECBAgQIBAtQUE7tXuX60jQIAAAQIECBCoiIDAvSIdqRkECBAgQIAAAQLVFhC4V7t/tY4AAQIECBAgQKAiAgL3inSkZhAgQIAAAQIECFRbQOBe7f7VOgIECBAgQIAAgYoICNwr0pGaQYAAAQIECBAgUG0BgXu1+1frCBAgQIAAAQIEKiIgcK9IR2oGAQIECBAgQIBAtQUE7tXuX60jQIAAAQIECBCoiIDAvSIdqRkECBAgQIAAAQLVFhC4V7t/tY4AAQIECBAgQKAiAgL3inSkZhAgQIAAAQIECFRbQOBe7f7VOgIECBAgQIAAgYoICNwr0pGaQYAAAQIECBAgUG0BgXu1+1frCBAgQIAAAQIEKiIgcK9IR2oGAQIECBAgQIBAtQUE7tXuX60jQIAAAQIECBCoiIDAvSIdqRkECBAgQIAAAQLVFhC4V7t/tY4AAQIECBAgQKAiAgL3inSkZhAgQIAAAQIECFRbQOBe7f7VOgIECBAgQIAAgYoIjKpIOzSDAAECBAgQINAUgdtuuy2eeOKJfpW16qqrxuabb96vPA4m0F8BgXt/xRxPgAABAgQIVFrggx/8YNxzzz2x9tprx1ve8pZYaqmlYtKkSXH11VcX7T744INj+eWXj2eeeSauueaaePHFF+Mzn/mMwL3S74r2aJzAvT36QS0IECBAgACBNhB4+umn4/7774+LLrooDj/88K4anXLKKUXgPnbs2Ljwwgtj3Lhxxb4777wzNttss9hpp526jvWEQKsEzHFvlaxyCRAgQIAAgY4T+OUvfxnHH398t6A9G3H99dcXbXnnO9/ZFbTnhk033bT423LLLYv9/kOglQIC91bqKpsAAQIECBDoKIGcFpOj641pypQp8ac//anYtPPOOzfuKp5/9KMfjZEjR8633QYCzRYwVabZosojQIAAAQIEOlZgr732mq/uv/rVr2LOnDnF9l122WW+/UccccR82wZrw+23316c6q1vfWtMnTq1mHP/1FNPxbve9a7IbfOmGTNmxFVXXRUHHnhg0aaco5/b9t577xgxYkTX4dOmTYucBnTvvffGKqusUvyqsPrqq3ftb3zy0ksvxbXXXhsHHHBAsfmOO+6I/OVizTXXjPRacsklGw/v9/OHH344nn322QXmy2sRRo8e3XXMrFmz4u677470yesRtt5661huueW69teftMKjXnYrHo24t0JVmQQIECBAgEBlBK677rqiLRm4brDBBkPerrwo9pOf/GSst9568W//9m9FkHziiSfGCiusEAcddFD8v//3/4rtJ510UsydO7eo76233hof/vCHiyA8L67NwPztb3977LHHHvG+970v8stJPV1yySVFO7///e/HmDFj4vLLLy+C8MMOOyzy14d6+slPfhL77LNPcd788pIX9L7pTW8qzp3TjfLLQX55yOB/ICkv/D3kkEPiu9/9bvz4xz/u+tttt92KNhx99NHdvnT89a9/je233z6+/e1vx3333Rcf+chH4o1vfGN861vf6qpGKzy6Cm/hEyPuLcRVNAECBAgQIND5AvX57e95z3vaojE5gv3v//7v8T//8z9Ffc4666xiXv4jjzxSBOpnnnlmnHvuuXHGGWfEJptsUgTz48ePL0bYn3/++SLIPe644yKD/RwZz9HyNdZYoygr82TAnyvo7LrrrsW2DNjXWWedYuWcP/zhD/GXv/wl8iLdHMV+4IEH4kc/+lHkCHeWef7558dWW20VN910Uxx55JHx0EMPxf7771+MfpedTpS/BGR5K664Ypf/lVdeGS+88EIsuuiicfHFFxePuTOD9pzOlO3KLxGZjj322OKLyFFHHRVrrbVWvPvd745WeBQna/F/RtS+if3zq1gfTvSVr3wlJk6cWHzzym9REoF2FDjrNxGbrRoxdtFytfvLkxHv2yhitaXL5b/4zxFLjo1YbrFy+e94ImLFJSJWWapc/kdfqNW9lvfd65TL/8fHI+6ZFLHO8n3PP/nZqXHI//e1HjN87OAt48xj393jPhsJECDQ7gIZmL75zW8uqpmB8n777VeqyjnqnMFnX1MGu8sss8wCD89R6EsvvbS4kDZXwWlM9X3rrrtu/O1vfyt2NbYlR9HnjeUefPDB2HjjjSMvtP3tb3/bWFzxhWDDDTcsRrBPOOGE4ktBHvD3v/+9GPnP548++mgxMp/PM+XqPBtttFHMnj07chQ/R/rLpO9973tx6KGHdmXNgD1/+Zg8eXJ8+tOfjlNPPbVrX47Cr7zyyt1G13Nnfun6xS9+EXlxcb1trfDoqkiLnhhxbxGsYgkQIECAAIHOF6hPk8nR4h122KF0g37zm98UI799LWCbbbZZaOCe01gy5SjyvClH0zOoz8A615lfeumlY7XVVus6LOe0z5u+/vWvx+uvv14E7/Puy1HvDJ4/8YlPxAUXXNAVuNfrsMgii3QL2jN/fuF573vfGz/84Q/jj3/8Y+nAvTFoz3JzEDmD9lzRJ6cM1VOOtuec/bwRVn2+fX1ffqnIlPPe66kVHvWyW/UocG+VrHIJECBAgACBjheoT5PJUeiFjYAvqLE5l/wNb3jDgg7ptq8+daXbxn68yKA5g+m8qDan0OTFm41TVRqf14vNOeqZ8qLSnlL9i0vOc8/17nNke2EpR+kzcM86NCPlFJ4cgZ93ikyWXQ/KcxpRT19M5j1/o0Hj8/pxrfCol132UeBeVk4+AgQIECBAoNICueLIDTfcULSxp9Vk+tP4XNmkp1VeeiujfoOn3vYvbHsGtvlFI+e093VVlwzGM+WFqz2lxlVl+hq45wWzmfpah57OW9+Wq9fkhaiZcuQ/b3zVmOpfDnIqTWNdG4/pz/NWePTn/D0dK3DvScU2AgQIECBAYNgL5Jz0V155pXDoaf32/gDlCid5kWRfU4725mh12TRz5sxiBZicytLTVJqeys2LOe+66654/PHaxU49pLwAtZ5y7nxfUk5pyVS/TqAveXo75uMf/3g88cQTRcDeOEWmfnz9F4BcjrIZqRUeA62XwH2ggvITIECAAAEClRSoT5NZdtllY4stthhQG/MizfpocV8KynMOJOWFpjlNJr9w5JSZvqQcwc4LcLPdeUHpvNNHcgnFTBm0L7FEbRWFPqT6hbH1FWr6kKXHQ379618Xc+vzl4T8EpSP9TR9+vTiAtz6Up0//elPi2kzeaHtvCmXlMybbOXc+4WlVngs7JwL2y9wX5iQ/QQIECBAgMCwFMgLHTPlmuB9DX57g8obIuVfK1JO6Zk3feMb3yiWfTzttNO6djUuJFi/oVTXztqTXOc9l5HMGzjlEpG777574+64+eabi9eNZdYPyLJzlL8xoM7R9lx3PdeJ7880oXqZ9cf81eM//uM/ipc50j7vFJn8opHTe3IN+/yClXe5zSUsc5nKxmsFcq368847L2655ZairFZ61Ove7EeBe7NFlUdgiAWmTY+4t7ac4yPPl6vIqzPLL6VZ7oxyESBAoP0ELrzwwuLOoVmznJ/ezimD0VwzvX7xa17AmYF7LpOYK6/UU666Uk833nhj7LjjjvWXxWOuPPO1r32tWJHlmGOOKVaCWWmllYp9L7/8crG2e45Uv//97++WL19kEJw3XTrnnHOKfa+99loRPOddV88+++z5ju/PhpzPnvPXM2DP540pg/pct/5zn/tcsTnXtM9fGXK6TB6fy0NmG/JLWN59Ndehr38Ja6VHYx2b+Vzg3kxNZRFoA4E5tTszjKn9nz1h8XKVee3FcvnkIkCAQBUEcjQ210S/7LLLupqTa5Avt9xyxTKDfZli0ZVxkJ5kcJqB++jRoyMD5ocffji+9KUvRQbf9ZQBba7GUk+5b6+99iqC+8ZpL7kaS87t/8AHPhDveMc7ijuW5gWmeRfVHNE+5ZRT6kV0e8zlInOOeX5RyLnhufxj3rQpR+nrwX+3DH188ec//zm++tWvFkfnxan560em/KKQXyYyoM/R9vyCkCl/1cibL2Vdn3zyyWL9+NyeFxdn8F4fgW+1R56zFUng3gpVZRIYYoG8+dSy48tV4pmXy+WTiwABAlUQyLt+5t83v/nNjmlOBso5dzvXbM/R5J4uHM3R8PzrS8rpJnlzovwCcOeddxYB+RFHHBELWukmA/csP+9OmsF01mmxxUreibChkrkme86370/adttti4tYc+32vJh1/fXXjwkTJnQrotUe3U7WxBcC9yZiKooAAQIECBAgMBQCGbBnsNzMtPbaa0f+9SflEpS9rXefI+Q5at6ftOqqtVuhl0w5dSj/mpXKeDTr3PVyBO51CY8ECBAgQIAAAQItE8jpOznvvq8pV7VpXIKyr/mqfJzAvcq9q20ECBAgQIBAZQXyRkOZXn311SFrY70OuUrN66+/HmPHju21LhMnTowPfvCDve63Y+ECAveFGzmCAAECBAgQINBWAvfff3/8/ve/L+qUF10eeuihsd5668WoUYMX2uWFsHnRaj1dcMEFRT16myqT66fnn1ReYPB6t3wd5SRAgAABAgQIEPg/gXvvvbe4SVLj3UOvu+66eOyxx2KgNzrqD/Kll14aOQc9p8BkyotIL7744jj88MMjl5aUmi8gcG++qRIJECBAgAABAi0T2HDDDSP/hjrlEpTS4AosMrinczYCBAgQIECAAAECBMoICNzLqMlDgAABAgQIECBAYJAFBO6DDO50BAgQIECAAAECBMoICNzLqMlDgAABAgQIECBAYJAFBO6DDO50BAgQIECAAAECBMoICNzLqMlDgAABAgQIECBAYJAFBO6DDO50BAgQIECAAAECBMoICNzLqMlDgAABAgQIECBAYJAFBO6DDO50BAgQIECAAAECBMoICNzLqMlDgAABAgQIECBAYJAFBO6DDO50BAgQIECAAAECBMoICNzLqMlDgAABAgQIECBAYJAFBO6DDO50BAgQIECAAAECBMoIjCqTSR4CBAgQIECAQNUEHn0h4uHaX7ukt68RMX50u9RGPdpBQODeDr2gDgQIECBAgMCQCzz1UsS9kyKWGjfkVYlJtbpstorAfeh7or1qIHBvr/5QGwIECBAgQGAIBRYfE/GGZYewAv936skvD30d1KD9BMxxb78+USMCBAgQIECAQNsKPPzww/HII4+0bf3qFZs+fXrcfPPN9ZeVeBS4V6IbNYIAAQIECBAg0HqB++67L7bddtv49a9/3fqTDfAM+QVjzz33jCuvvHKAJbVPdoF7+/SFmhAgQIAAAQIE2lbgjjvuiHe+851x0EEHxZFHHtm29axXbP31149LLrkkDjnkkPjOd75T39zRjwL3ju4+lSdAgAABAgQItF7g/vvvj+233z622267OOuss1p/wiadYZdddonzzz8/Dj/88CKIb1KxQ1aMwH3I6J2YAAECBAgQIND+AnPnzo2jjjoq8vGb3/xmLLJIZ4WPRxxxROy8885x7LHHxuTJk9sffAE17Cz5BTTELgIECBAgQIAAgeYLZLD++9//PiZOnBjLLLNM808wCCWedtppMWXKlKINg3C6lp1C4N4yWgUTIECAAAECBDpbYNKkSXHCCSfE0ksvHccdd1zHNmaLLbaIPfbYIy6//PK45pprOrYd1nHv2K5TcQIECBAgQIDA/AKzZ8+OQw89NGbNmjX/zoVsybngu+66a9dRZ5xxRrz44ovFVJmlllqqa3snPskLan/2s5/F8ccf362NndQWgXsn9Za6EiBAgAABAgQWIvDHP/4xLrvsslhllVVi3333jU033TSWXHLJePLJJ+NjH/tYkTtXhskg/ZVXXolc4vHss8+O559/vghq68XnF4ArrriieLnNNtvUN3fs49Zbb13U/Z577om77rorNtlkk45ri8C947qsvSs8Z27E9P5/wZ+vUeMWnW+TDcNEYM6c2nto9sAb6z00cEMlECDQmQK/+MUvYqONNorrr78+Vl555a5GfPnLX+56fthhh8VOO+1UvN57771j1KhRkaPrb33rW7uOueGGGyKnymSqB71dOzvwyQorrBDrrLNOPPjgg8UXG4F7B3aiKjdX4JlpEd/588DL/K/tIka6AmPgkB1YwlMvRVxyx8AqPqKW/fjtB1aG3AQIEOhUgRtvvLEITBuD9mzLddddVzRp3LhxxbKOje3bfffd409/+lO3FWNyPnimDHjXXXfdxsM79nl+AcnAPdt2+umnd1w7jLh3XJe1f4VHj4x4y6rl6jmzNtp6+xPl8spVHYEcLd/kX4NE/WrY67VffO56ql9ZHEyAAIFKCXzoQx8qRtwbGzV9+vTIgD5T3kRp7Nixjbtj+eWXj4MPPrjbtquuuqp4XZWgPRuTI+6ZHn300cgbSr3lLW8pXnfKf4xpdkpPdVg9c4nXMn8jcqhUIlATKPP+KfJ4D3n/ECAwzAX222+/+QRyOcdXX3212J5rms+bMnDPKTP1NHXq1GLOe77u9ItS622aty0PPfRQ466OeC5w74huUkkCBAgQIECAQHmB+jSZLKGnwH3ekvNC1npaUOCeI/m5zvtHPvKRYh58TquZNyC++uqri+k2J598cr3IIXtsbMsTT3TeT/wC9yF76zgxAQIECBAgQGBwBPJC1Uyrr756bLDBBgs9aWNQ2xjszpsxL2rdc88948ADD4zM8+yzz8YFF1zQ7bC80+pzzz0X5557bnH31W47B/lFY1sa2zjI1Sh9OoF7aToZCRAgQIAAAQLtLzB58uRi+cOsaV9G2/O4xhH3vPlSb2nkyJGx0korFfPmzznnnOKwvPBz7tzaMnP/l3bZZZfiYtAJEybEiCGeE9sYuDe2sV7Xdn8UuLd7D6kfAQIECBAgQGAAAjnaXg+k+xq4z5w5s+uMOWLel7TPPvsUU2Ief/zxuO2227plybXk22Et+Ma2NLaxW2Xb+EXfeqKNG6BqBAgQIECAAAECvQvUp8nk6PiOO+7Y+4ENe1Zd9V/Lw+WFqn1JGRTntJlMeYfSxpR1OOqooxo3DcnzxrY0tnFIKlPipAL3EmiyECBAgAABAgQ6QSBH2vOGTJne9ra3xYKmvTS2pzGobQx2G4/p6fluu+1WbG68GHZO7c56v/vd72LbbbftKcugbmtsy2qrrTao527GyQTuzVBUBgECBAgQIECgDQXuuuuuyDnumfo6TSaPbQxqG4Pd3LeglGvE5zz2nCrz2muvFYdeeeWVse+++y4o26Dte+ml2l3+/i81trG+rd0fBe7t3kPqR4AAAQIECBAoKdA48p0XifY1LbfccjF+/Pji8P4E7plvww03jJw/fs899xSPuRxkrjozb3r99deLi1avuOKKePjhh+M///M/45JLLikOmz17dlx00UXF9JqPfexjxc2S6vnzV4RbbrklJk6cGLNmzSry5M2jcknKvLHSglJjW9Zcc80FHdqW+wTubdktKkWAAAECBAgQGLhAfX57ruiy+eab96vAXXfdtTj+scce61e+7bbbrjj+3nvvjfPPPz8+/OEPz5c/y9x///3joIMOKubDn3rqqcXjN77xjeJGUbn9jW98Y5x99tmx+OKLxxZbbBE//vGPi3Iuu+yyyJtMfeUrX4m8S+z9998fK664Ylx44YVFGxe0WkxeOJspv2BsueWWxfNO+o/AvZN6S10JECBAgAABAn0UyLXT846pmXJ+eeOKKn0pIoPnTBlkP/XUU33JUhyz9dZbF4+/+c1vipH0DLrnTTna/fWvf73YfPfdd8d3v/vdePDBB+Oaa66JL37xi8Va8/kFYMkll4xPfvKTseiii0aOvGfK0fX6XPrjjjsuPvvZz0YuRZmj9c8//3ycfvrpxXE9/SdH6jPll4Zcg77TUufVuNOE1bdfAvVlX5+ZFjHS18p+2TmYAAECBAg0Chx99NGRdzbNNHr06MZdfXq+++67xxJLLBEvv/xy3HzzzcUod18y1gP1H/7wh/HAAw/0miXXf89UX+kmA+n8+9rXvlaMnB9zzDFdefPC2mxLzpsfN25cjB07tti3zjrrdB2To/A5b/0Pf/hD17bGJzm/PafvZKp/KWnc3wnPBe6d0EvDqI6z5vyzsZf9pXyj/3XLh/JlyEmAAAECBDpV4Atf+ELk3PZf/epXXU3IIPrYY4+NtdZaK3KUui8pg+O99947vve978VNN93U58A9z5Gj+2eeeWasssoqvZ6qfjOm+mMeOGXKlJg0aVJ84AMf6HFefK+F/d+OPHdvvw7ceuutkSvc5N1j3/GOdyysqLbcL3Bvy25RqbfWVmhaZEQ5h1seLZdPLgIECBAgUAWBj3/845F/zUgnnXRS5MWjGfhnIN6XkfscZd9pp516nNu+sDqNGTOmOOTOO+/sMXDPC1MbA/15y3vooYdi4403nndz8TrbkenTn/70AssoDmrT/5iM0KYdo1oECBAgQIAAgaEWWH/99eOEE06IvKgzL/5cWMrA+vOf/3yxIszCju1pf65k86Y3vSkuuOCCePHFF7sd8qMf/SgyoO8t5ReGHG3PXwnmTTl/PufR57z5I444Yt7dHfNa4N4xXaWiBAgQIECAAIHBF8iLQzOYzoA8l3BcUMoLRfMOqQuaIlPPP21a7YK2Wnr22Wfrm4rH448/vrjINC+ozaUkb7/99uIC1FyTfrPNNut27B133NH1+gc/+EHkF40jjzyya1v9yWmnnRZ559hctWZBI/b149v1UeDerj2jXgQIECBAgACBNhDI6Ss5Ap43cvrUpz7VVaMcXf/MZz5TLPk4Y8aMYjWXNdZYI971rnd1HdPbk7/97W/FnPvcnyPpOS+/HsjniHiWmyPoeYFsXuya895z5H/e9P3vf79Ybeawww6LX//613HDDTcUK9A0HvfLX/4yLr300jj11FOLLyCN+zrtuTnundZj6kuAAAECBAgQGGSBvCNq3gH1gAMOiLXXXruYv56BfAbYGcDnqHxOpenrHVLXW2+9+M53vlP89dSUU045pQjU//73v8cb3vCGYi33no7Ldd5zxD5Xmll22WXnOySn1mSdTjzxxOJvvgM6bIPAvcM6THVbL1C74DxyOcqyq9PMnN36OjoDAQIECBAYbIG99torfv7zn8f73ve+YmWWPffcM3LEO+96euihh0az70SaI/0bbbTRQpu56qqr9njMP/7xj2LEPr9U5PSbKiSBexV6URuaKjCzFrj/9uHy68hPnxUxYbGmVklhBAgQIECgLQR22GGHuPbaa4v54lmhvBnSUKT6XPtcmz3vgtpTygtdzzrrrCGrY091Gug2gftABeWvpMC6y0cs+c97O/S7fX9+vN9ZZCBAgAABAh0jsNVWWw1pXX/6058W69RnJfKi07wYtqclICdMmFCpoD3bK3BPBYkAAQIECBAgQKAjBDbddNO48cYbu+q62GLD52dugXtXt3tCgAABAgQIDHeBvIP3tOnDXaG9258r1wzXJHAfrj2v3QQIECBAgEA3gUVHRrw2M+LB7suKdztmsF6MqUVoZe8gPlh1dJ7BFxC4D765MxIgQIAAAQJtKLDF6hH5JxFoVwE3YGrXnlEvAgQIECBAgAABAg0CAvcGDE8JECBAgAABAgQItKuAwL1de0a9CBAgQIAAAQIECDQICNwbMDwlQIAAAQIECBAg0K4CAvd27Rn1IkCAAAECBAgQINAgIHBvwPCUAAECBAgQIECAQLsKCNzbtWfUiwABAgQIECBAgECDgHXcGzA8JUCg+QLPvxrx18l9LzePnzu378c7kgABAgQIDBcBgftw6WntJDBEAk9MjfjdI30/+fTaXQtnC9z7DuZIAgQIEBg2AgL3YdPVGkpgaAQmjI9Yf8W+n/vplyKefLHvxzuSAAECBAgMFwFz3IdLT2snAQIECBAgQIBARwsI3Du6+1SeAAECBAgQIEBguAgI3IdLT2snAQIECBAgQIBARwsI3Du6+1SeAAECBAgQIEBguAgI3IdLT2snAQIECBAgQIBARwsI3Du6+1SeAAECBAgQIEBguAgI3IdLT2snAQIECBAgQIBARwsI3Du6+1SeAAECBAgQIEBguAgI3IdLT2snAQIECBAgQIBARwsI3Du6+1SeAAECBAgQIEBguAgI3IdLT2snAQIECBAgQIBARwsI3Du6+1SeAAECBAgQIEBguAgI3IdLT2snAQIECBAgQIBARwuM6ujaqzwBAgQIECBAoA0E5s6d22MtRowY0eN2GwmUERC4l1GThwABAgQIECDQIDBm6zMbXv3r6b7vfnNc9rm9/7XBMwIDEDBVZgB4shIgQIAAAQIECBAYLAGB+2BJOw8BAgQIECBAgACBAQgI3AeAJysBAgQIECBAYLgJPPzww/HII48MuNnTp0+Pm2++ecDlDKcCzHEfTr2trQQILFRgTu36st8P/N+jeOuqEYuPWejpHECAAIGOErjvvvtixx13jNNOOy2OPPLIAdU9vwDsueeeccEFF8S+++47oLKGS2aB+3Dpae0kQKBPArkwxC2PRSy/eMQiJReDeHZaxJuWF7j3CdxBBAh0jMAdd9wR73nPe+Kwww4bcNCejV5//fXjkksuib333jumTZtWlNsxGENUUYH7EME7LQEC7S2w5jIRi44sV8cpr5bLJxcBAgTaVeD++++P7bffvhhtP+uss5pWzV122SXOP//8OPzww2PUqFFxyCGHNK3sKhZkjnsVe1WbCBAgQIAAAQJNEsg16o866qjIx29+85uxyCLNDR+POOKI2HnnnePYY4+NyZMnN6nW1SymufLVNNIqAgQIECBAgMCwFchg/fe//31MnDgxllmm9nNkC1LOmZ8yZUpxjhYUX5kiBe6V6UoNIUCAAAECBAg0V2DSpElxwgknxNJLLx3HHXdccwtvKG2LLbaIPfbYIy6//PK45pprGvZ42ihgjnujhucECBAgQIAAgQ4XmD17dhx66KExa9asfrck55rvuuuuXfnOOOOMePHFF4upMksttVTX9lY8yVVqfvazn8Xxxx/frQ6tOFenlilw79SeU28CBAgQIECAQA8Cf/zjH+Oyyy6LVVZZpVhmcdNNN40ll1wynnzyyfjYxz5W5DjooIOKC0JfeeWVyCUezz777Hj++eeLoLleZH4BuOKKK4qX22yzTX1zyx633nrroux77rkn7rrrrthkk01adq5OLVjg3qk9p94ECBAgQIAAgR4EfvGLX8RGG20U119/fay88spdR3z5y1/uep5LOu60007F61yOMVd0ydH1t771rV3H3HDDDZFTZTLVg+qunS14ssIKK8Q666wTDz74YPHFQ+A+P7I57vOb2EKAAAECBAgQ6FiBG2+8sQh8G4P2bMx1111XtGncuHGx3XbbdWvf7rvvHjvssEO3FWNyvnmmDKjXXXfdbse36kX9C0L93K06T6eWK3Dv1J5TbwIECBAgQIBADwIf+tCHihH3xl3Tp0+PDOgzvfOd74yxY8c27o7ll18+Dj744G7brrrqquL1YAXtebIccc/06KOPRt7wSeouIHDv7uEVAQIECBAgQKCjBfbbb7/56p/LOb766j/vDpdrps+bMnDPKTP1NHXq1GLOe75u9UWp9XPOe66HHnqocZfnNQGBu7cBAQIECBAgQKDiAvVpMtnMngL3eZufF7LW04IC9xzJz3XeP/KRjxTz4HNazbwB99VXX11Mtzn55JPrRfb62HiuJ554otfjhusOgftw7XntJkCAAAECBIaNQF6ommn11VePDTbYYKHtbgyaG4PpeTPmRa177rlnHHjggZF5nn322bjgggu6HZZ3Wn3uuefi3HPPLe6+2m3nPC8az9VYh3kOG7YvBe7Dtus1nAABAgQIEBgOApMnTy6WV8y29mW0PY9rHHHPmy/1lkaOHBkrrbRSMW/+nHPOKQ7LC0vnzp3blWWXXXYpbqw0YcKEGDFiRNf2np40Bu6Ndejp2OG4TeA+HHtdmwkQIECAAIFhI5Cj7fVAuq+B+8yZM7t8csS8L2mfffYppsQ8/vjjcdttt3XLkmvJ92Ut+MZzNdahW2HD+EXfemIYA2k6AQIECBAgQKCTBerTZHJ0fMcdd+xTU1ZdddWu4/JC1b6kDLpz2kymvANqY8o6HHXUUY2benzeeK7GOvR48DDcKHAfhp2uyQQIECBAgMDwEMiR9rwhU6a3ve1tsaBpL40ijUFzYzDdeExPz3fbbbdic+PFsHPmzInf/e53se222/aUpdu2xnOtttpq3fZ5YVUZ7wECBAgQIECAQGUF7rrrrsg57pn6Ok0mj20MmhuD6dy3oJRrxOc89pwq89prrxWHXnnllbHvvvsuKFvXvpdeeqnreWMdujYO8ydG3If5G0DzCRAgQIAAgeoKNI5850WifU3LLbdcjB8/vji8P4F75ttwww0j56ffc889xWMuB5mrzvQlNZ5rzTXX7EuWYXWMwH1YdbfGEiBAgAABAsNJoD6/PVd02XzzzfvV9F133bU4/rHHHutXvu222644/t57743zzz8/PvzhD/c5f17Ymim/AGy55ZZ9zjdcDhS4D5ee1k4CBAgQIEBgWAnk2ul5x9RMOb+8ccWWvkAcdNBBxWEZuD/11FN9yVIcs/XWWxePv/nNb+Lhhx+OLbbYos95b7nlluLY/fffP3KNeKm7gMC9u4dXBAgQIECAAIFKCBx99NGRdzbNNHr06H63affdd48llliiyHfzzTf3OX89UP/hD38YJ5xwQp/z5fz2nF6Tqf6loc+Zh8mBAvdh0tGaSYAAAQIECAwPgS984QvFso8ZONdTPj/22GPjS1/6Un3TQh/Hjh0be++9d3HcTTfdtNDj6westdZaxej+mWeeGausskp980Ifb7311sgVaPLuru94xzsWevxwPEDgPhx7XZsJECBAgACBygp8/OMfj1/+8pfFTZdyOcj8y4tFzz333DjuuOP61e6TTjqpGK3PwH/GjBl9yvvAAw/ETjvt1K+57VnwFVdcUZT/6U9/eqF3WO1TRSp4kMC9gp2qSQQIECBAgACBZgisv/76xXSXvGj0wgsvXGiR+SXh85//fFx00UULPbbxgAcffDC++93vRl7YesQRRzTu8rxBQODegOEpAQIECBAgQIBAd4FPfvKT8aY3vakIyF9//fXuO+d59dnPfra4Q2p/pshkEaeddlrknV2/8Y1vGG2fx7TxpcC9UcNzAgQIECBAgACBbgJjxoyJCy64oLiR06c+9amufTm6/pnPfKZY8jGn0Zx++umxxhprxLve9a6uY/ryJKf1XHrppXHqqacWXxD6kme4HmOdneHa89pNoKICM2f/s2GX3F6ugbV/hyQCBAgQmEcg74iad0A94IADYu211y7mr+cdWTNwzwA+R+VzKk1f75BaL/7OO+8s8px44omRf9KCBQTuC/axlwCBDhOYUwu8M/YeU/LTLfNLBAgQIDC/wF577RU///nP433ve1+x8suee+4Z3//+94u12g899NDo751O//GPf0QuOZlB//HHHz//CW2ZT6DkP23zlWMDAQIE2kpghX8uPdzvOs2aE/HIC/3OJgMBAgSGhcAOO+wQ1157bTEfPRt88MEHl273+PHj46yzzhpQGaVP3qEZBe4d2nGqTYAAAQIECBAYCoGtttqqKaedMGGCoL2fki5O7SeYwwkQIECAAAECBAgMhYAR96FQd04CBAgQIECgUgKP/+w/e2zP2NFCrR5hbCwl4N1Uik0mAgQIECBAgMC/BFaasPi/XnhGoEUCpsq0CFaxBAgQIECAAAECBJopIHBvpqayCBAgQIAAAQIECLRIQODeIljFEiBAgAABAgQIEGimgMC9mZrKIkCAAAECBAgQINAiAYF7i2AVS4AAAQIECBAgQKCZAgL3ZmoqiwABAgQIECBAgECLBATuLYJVLAECBAgQIECAAIFmCgjcm6mpLAIECBAgQIAAAQItEhC4twhWsQQIECBAgAABAgSaKSBwb6amsggQIECAAAECBAi0SEDg3iJYxRIgQIAAAQIECBBopoDAvZmayiJAgAABAgQIECDQIgGBe4tgFUuAAAECBAgQIECgmQIC92ZqKosAAQIECBAgQIBAiwQE7i2CVSwBAgQIECBAgACBZgoI3JupqSwCBAgQIECAAAECLRIQuLcIVrEECBAgQIAAAQIEmikgcG+mprIIECBAgAABAgQItEhA4N4iWMUSIECAAAECBAgQaKaAwL2ZmsoiQIAAAQIECBAg0CIBgXuLYBVLgAABAgQIECBAoJkCAvdmaiqLAAECBAgQIECAQIsEBO4tglUsAQIECBAgQIAAgWYKCNybqaksAgQIECBAgAABAi0SELi3CFaxBAgQIECAAAECBJopIHBvpqayCBAgQIAAAQIECLRIQODeIljFEiBAgAABAgQIEGimgMC9mZrKIkCAAAECBAgQINAiAYF7i2AVS4AAAQIECBAgQKCZAgL3ZmoqiwABAgQIECBAgECLBATuLYJVLAECBAgQIECAAIFmCgjcm6mpLAIECBAgQIAAAQItEhC4twhWsQQIECBAgAABAgSaKSBwb6amsggQIECAAAECBAi0SGBUi8pVLAECBIatwNy5ETNmRUyv/ZVNi9aGVRYxtFKWTz4CBAhUUkDgXslu1SgCBIZSYNaciEv/MrAa7LtxxDrLDawMuQkQIECgWgIC92r1p9YQINAOAiMiNlwxYrHR5Srz18nl8slFgAABAtUWELhXu3+1jgCBIRIYWQveR5ac6lLLKhEgQIAAgfkESv6zMl85NhAgQIAAAQIECBAg0EIBgXsLcRVNgAABAgQIECBAoFkCAvdmSSqHAAECBAgQIECAQAsFzHFvIW4nFj1rdsSrM8vX/JXp5fPKSYAAAQIECBAg0LuAwL13m2G559EpEVfePbCm50V5EgECBAgQIECAQHMFBO7N9axEaUuMidho5XJNeXZaxCPPl8srFwECBAgQIECAQO8C5rj3bmMPAQIECBAgQIAAgbYRELi3TVeoCAECBAgQIECAAIHeBQTuvdvYQ4AAAQIECBAgQKBtBATubdMVKkKAAAECBAgQIECgdwGBe+829hAgQIAAAQIECBBoGwGBe9t0hYoQIECAAAECBAgQ6F1A4N67jT0ECBAgQIAAAQIE2kZA4N42XaEiBAgQIECAAAECBHoXELj3bmMPAQIECBAgQIAAgbYRELi3TVeoCAECBAgQIECAAIHeBQTuvdvYQ4AAAQIECBAgQKBtBATubdMVKkKAAAECBAgQIECgdwGBe+829hAgQIAAAQIECBBoGwGBe9t0hYoQIECAAAECBAgQ6F1A4N67jT0ECBAgQIAAAQIE2kZA4N42XaEiBAgQIECAAAECBHoXELj3bmMPAQIECBAgQIAAgbYRELi3TVeoCAECBAgQIECAAIHeBUb1vsueThR46fWI514pX/NJL0fMnVs+v5wECBAgQIAAAQKtERC4t8Z1yEp96PmIXz0YMa5kz06fFTHK7zBD1n9OTIAAAQIECBDoTaBkeNdbcba3g8DSYyPWW6FcTR6fEvHCAEbsy51VLgIECBAgQIAAgYUJGFtdmJD9BAgQIECAAAECBNpAQODeBp2gCgQIECBAgAABAgQWJiBwX5iQ/QQIECBAgAABAgTaQEDg3gadoAoECBAgQIAAAQIEFibg4tSFCQ3y/menRTz/avmTFss5ls8uJwECbSAwa07EE1Mj8rFsWmmJiKXHlc0tHwECBAi0o4DAvc165b5nIm57orac46LlKvbqjIjxo8vllYsAgfYQmDE74q6nI+6vfR6USa/UPgfevU7EW1Ytk1seAgQIEGhXAYF7G/bMMuMj1p5QrmJ/q/1DP5BRunJnlYsAgWYKjKgVtuYyEflZUCbl54BEgAABAtUTMMe9en2qRQQIECBAgAABAhUUELhXsFM1iQABAgQIECBAoHoCAvfq9akWESBAgAABAgQIVFBA4F7BTtUkAgQIECBAgACB6gkI3KvXp1pEgAABAgQIECBQQQGBewU7VZMIECBAgAABAgSqJyBwr16fahEBAgQIECBAgEAFBQTuFexUTSJAgAABAgQIEKiegMC9en2qRQQIECBAgAABAhUUELhXsFM1iQABAgQIECBAoHoCAvfq9akWESBAgAABAgQIVFBA4F7BTtUkAgQIECBAgACB6gkI3KvXp1pEgAABAgQIECBQQQGBewU7VZMIECBAgAABAgSqJyBwr16fahEBAgQIECBAgEAFBQTuFexUTSJAgAABAgQIEKiegMC9en2qRQQIECBAgAABAhUUELhXsFM1iQABAgQIECBAoHoCAvfq9akWESBAgAABAgQIVFBA4F7BTtUkAgQIECBAgACB6gkI3KvXp1pEgAABAgQIECBQQQGBewU7VZMIECBAgAABAgSqJyBwr16fahEBAgQIECBAgEAFBQTuFexUTSJAgAABAgQIEKiegMC9en2qRQQIECBAgAABAhUUELhXsFM1iQABAgQIECBAoHoCAvfq9akWESBAgAABAgQIVFBA4F7BTtUkAgQIECBAgACB6gkI3KvXp1pEgAABAgQIECBQQQGBewU7VZMIECBAgAABAgSqJyBwr16fahEBAgQIECBAgEAFBQTuFexUTSJAgAABAgQIEKiewKjqNWloW3T30xH/eLF8HSa9HLHIiPL55SRAgMBABe6dFPHYlIGVsvrSERuvPLAy5CZAgACB7gIC9+4eA371aO0fuyemRiw5plxRU1+PWHpcubxyESBAoBkCj9cGH/KzbKmx5Up7aXrEnLkC93J6chEgQKB3AYF77zal9+Q/dqvVRpvKpAzcJQIECAy1wJK1z7HVlylXiycG8KtjuTPKRYAAgeEhYI778OhnrSRAgAABAgQIEOhwAYF7h3eg6hMgQIAAAQIECAwPAYH78OhnrSRAgAABAgQIEOhwAYF7h3eg6hMgQIAAAQIECAwPARenDo9+1koCBIaRwGszI/7yVMQjL5Rr9DPTIhYvuTJWnvHVGRFTX4v44d3lzp+5VloiYus3lM8vJwECBKooIHCvYq9qEwECw1pg9pyIWbW/GbPLMWTgPZDAfWbt3LNry0GWPf+02nKSr9e+fAjcy/WfXAQIVFdA4F7dvtUyAgSGsUAuS7vSkuUAcsR9oGn8ohGrLFWulGdqN6KbMatcXrkIECBQZQFz3Kvcu9pGgAABAgQIECBQGQGBe2W6UkMIECBAgAABAgSqLCBwr3LvahsBAgQIECBAgEBlBATulelKDSFAgAABAgQIEKiygItT5+ndGx6KePqleTb24+ULr0YsM74fGRxKgAABAgQIECBAoA8CAvd5kJ6aWluGrLaawRK1FRnKpOkll18rcy55CBAgQIAAAQIEho+AwL2Hvl6yFrSvULv5R5n0dC3wlwgQIECAAAECBAg0W8Ac92aLKo8AAQIECBAgQIBACwQE7i1AVSQBAgQIECBAgACBZgsI3JstqjwCBAgQIECAAAECLRAQuLcAVZEECBAgQIAAAQIEmi3QdhenXv6Xf67qUrahU1+LWGx0xKiR5Up4sZZ/taXK5ZWLAAECBAYu8MqMiCm1pXUv/nO5smbWVvd6bWZELjRQNo2t/ev4/s3K5h76fD+6J+LF18vXY0Qt67ZrRaw9oXwZchIg0HyBtgvccznGVWqB8+iSNXtuWsTYRSPG14L3Mun5V8rkkocAAQIEmiUwe07EIrXfg8sG3i/UPsczeC+bf0ZtSeCnBnA/j2Y5DKScvB9JLmtc9t/C/Lf41dqXH4kAgfYSKBket7YRS48r/2Hz8PP//LDOMsqkx14ok0seAgQIEGimwKK1wH25xcqV+Hot4JxWG7Uvm//VWt5OD9xTbqla4L5UyX8LcxBMIkCg/QTMcW+/PlEjAgQIECBAgAABAvMJCNznI7GBAAECBAgQIECAQPsJCNzbr0/UiAABAgQIECBAgMB8AqXmuD/55JNx//33z1dYMzZMfqx2QU1tNYG8wLRMeuap2vz4lyOmllxNYPLTtYuiptRWJCg5t3LypIgZ4yNmL1mm9hGTnq21vdYro2oXBpVJT9fm+OeFXYuVvMj26dp5c2Wex6aXOfs/V4J45sVa/trFXWVSrgTxzDMRj9fyj8hlDUqkZ56s5a+tpjBmAO+hxWt+i48pcfJalsm192D23yu190GZlO+hWbX338wlyuSuvYdqftNqdR9Rex+XSZOeq+Wq2Y+t/X/U1/T8Cy/H3NdqJ+4hTX328Xjsob5/Xjxbm1s7uXZh3WMlL4ybVuv7Z2r/HzxWuzixTMr/f56pfQ48XpvnXHZ1qvwc+kftc2xcyYvkM//YmsFLJecn5+fY3Nr7Z/riZQRq/pNrq3vVPkPnlrzmJz/HRtdW9lq05AWeT9fOO6PWf4sfgO7QAAAKzUlEQVTXDMukfP88X8tb9nMs58hPrv1/0KJ/5so0qd95JtX+LY2a44sl/y3Mz4GHaw6jap/nrUiL1K4+Xm+99VpRtDIJVFpgxNxa6msLr7rqqpg4cWI8/vjjfc3iOAIDEhg1alTk3+uv16IxiUBFBcaPHx/Tp0+P2bNLftuoqItmVUdg0UUXra0UtEjxPs9WjRkzxud6dbpXSwZRoF8j7vvss0/MnDkzrr322kGsolMNZ4H8deepp56KLbbYYjgzaHuFBebMmRO33HJLvPnNb44JEyyaXeGuHtZNe/DBB4tAfaONNiocMpCXCBDov0C/Rtz7X7wcBAYm8O1vfzsuuuii+N3vfjewguQm0KYCORiSwcx5550XO+64Y5vWUrUIDEzg5JNPjn/84x9x8cUXD6wguQkMcwEXpw7zN4DmEyBAgAABAgQIdIaAwL0z+kktCRAgQIAAAQIEhrmAwH2YvwE0nwABAgQIECBAoDMEBO6d0U/DtpZ5AdPYsSXXMxu2ahreSQIjauue5ns8V0+SCFRVID/LcyUZiQCBgQm4OHVgfnITIECAAAECBAgQGBQBI+6DwuwkBAgQIECAAAECBAYmIHAfmJ/cBAgQIECAAAECBAZFQOA+KMxOQoAAAQIECBAgQGBgAgL3gfnJTYAAAQIECBAgQGBQBCxjMCjMTtKTQN7q/b777ounn346Nt9881h66aV7OqzHbVOmTCnupvrCCy/EWmutFdtuu20ssojvoT1i2TikAvleve2222LllVeO9ddfv/T79NZbb40VV1yxeL8PaYOcnMA8AgP5LJ81a1b89a9/7Vbi5MmTi38TlllmmW7bvSBAIGLkqbUEgsBgC2TA/tGPfjSWX375WG211eLLX/5yPP7448WH9cLqcvfdd8cxxxwTzzzzTPz2t7+N66+/Pu69997YaaedIpfWkwi0g8DcuXPjc5/7XPzqV7+KrbbaKu66664488wzi+dLLLFEv6r4wAMPxMSJE2PttdeON7/5zf3K62ACrRQYyGd51is/v0888cS4+uqru/6ee+652H///VtZbWUT6FgBI+4d23WdW/H8UD7ppJPive99b9eH85prrhn77rtvrLrqqrHHHnv02rgcnbn55pvjwgsvjFVWWSVef/31oqw//elPkSOSW2+9da957SAwmALf/va3i1+Frrrqqhg/fnxssskmke/9E044Ib7+9a/HYost1qfq5Hv87LPPjtmzZ/fpeAcRGCyBgXyW1+v44x//OE455ZSuX6Jy8GWdddap7/ZIgMA8AuYWzAPiZesFLr744njxxReLwL1+tmWXXTbe/e53x/nnnx8zZ86sb57v8Z577olDDz20CNpzZ9645qCDDiqOy59rJQLtIJABTb7Pd9tttyJor9dpv/32i0cffTR+8pOf1Dct9DH/n9h7772L4/yitFAuBwyiwEA+y7Oaf/jDH2LjjTcufi3dYYcdIv/y34E11lhjEFvhVAQ6S0Dg3ln91fG1zVHDnN6y+uqrRwbrjWnDDTeMl19+OW655ZbGzd2eb7bZZt0CodyZd+TLst7+9rd3O9YLAkMlcMMNN0ROlclR9saUvyjltRzXXXdd4+Zen994442x3HLLFXPjez3IDgJDIDDQz/Ks8mWXXRbXXHNN/Nd//Vf84Ac/iOnTpw9BS5ySQGcJCNw7q786vrZPPfVUMdqe01zmTfVtOV+9r+m1116LnIrwxS9+0S3j+4rmuJYL1N/D9fd04wnzItWHHnqomObVuH3e5zlqf+2118Yhhxwy7y6vCQy5wEA/y3NhgVyYIH99zZH3c889Nz7wgQ8scOBmyButAgTaQEDg3gadMJyqkB/WmXLO77wpp71kyotOF5ZyOs3//u//xoEHHljMI86RyZz/LhFoB4H6+3zcuHHzVaf+Pn/22Wfn21ffkKP1X/nKV+LYY4+NkSNH1jd7JNA2AvX3eNnP8vyV9IorriimjZ1xxhmxwQYbxKRJk+ITn/hE8cW2bRqqIgTaTEDg3mYdUvXqvPLKK0UTR48ePV9Tc8pLpr78XJrLhWUAlPMhM9/FtfnE3/rWt+Yr0wYCQyFQf5+PGTNmvtPX3/sLep/ntIFc4jRH5yUC7ShQf4/X38+NdezPZ3ku+bjNNtvEeeedV4y45wDMV7/61cbiPCdAoEFA4N6A4WnrBXK+bqYZM2bMd7J6INOXC5NyCclcfSaXyPva174WubxeBjtW3piP1YYhEFjQ+zxXicmLTPM6j57Sgw8+WIw4vuc97+lpt20E2kJgQe/x/nyW1xszatSoOProo+MNb3hDsbxv/uokESAwv4DlIOc3saWFAnkDmUyvvvrqfGepb8sP7v6kN77xjcXoZK4DnDe7qf+D0p8yHEugmQILe5/nSHpPo/FZh1xFJvf993//d1eVpk2bVjz/+c9/XqwHn/cseNvb3ta13xMCgy2wsPd41qe/n+WZJ39puvTSS4vVxXoazc9jJALDWcCI+3Du/SFo+1JLLVUs5ZgXNs2b6nPb806o/U15U5ocdRe091fO8a0QyDukZurpfZ5z2xcU0GTeHH3MUcv6X32J1JxGkNtcz9GKXlNmfwRa9Vk+YcKEYjlIQXt/esOxw0nAiPtw6u02aWteUJoX3uWKAo1zeO+///5i+sC6667b75refvvtsfPOO/c7nwwEWiGQ61HnTZZytYztt9++6xR58V2uopHXZvSWjjrqqPl25V2Fc3WZvGnZXnvtNd9+GwgMhUArPsvvvPPO2H333YeiOc5JoCMEjLh3RDdVq5J5U5q8U2r+HFpPuULBTTfdFMccc0zXHfRyX45O5g2W8k6pmfKYCy64IJ588snidf4ng5oMhj70oQ91bfOEwFAK5MV5OV83b+eeF1LXU954KUfUM7BvTHmX1fe///3djm3c7zmBdhQYyGd5fqbnAM4TTzzR1bS8K3b+onTAAQd0bfOEAIHuAkbcu3t4NQgCuRpMrhrwuc99Lj71qU8Vgcyf//zn+OhHP1rMb2ysQgbk+cH+97//vdicF5/mLbK/973vxVve8pbIKTJ5kd8555xjHfdGOM+HXGDXXXctLkI9+eSTi5uD5ZfTqVOnxplnnjnfezXf3/llNK/RqM8dHvIGqACBhQgM5LM878GRS/peeeWVsfXWW8cKK6wQa6+9dpx++unF/zcLObXdBIatwIjaldsu3R623T/0Dc8VNurByiKL9PwDUM59zzV/c95vPeXNafIOlI3b6vs8Emg3gZwik9dgLLbYYj1WLeesZ2CfwYtEoBMFynyWv/TSS8Wvqssvv3wsueSSndhsdSYw6AIC90End0ICBAgQIECAAAEC/RfoeYiz/+XIQYAAAQIECBAgQIBACwUE7i3EVTQBAgQIECBAgACBZgkI3JslqRwCBAgQIECAAAECLRQQuLcQV9EECBAgQIAAAQIEmiUgcG+WpHIIECBAgAABAgQItFBA4N5CXEUTIECAAAECBAgQaJaAwL1ZksohQIAAAQIECBAg0EIBgXsLcRVNgAABAgQIECBAoFkCAvdmSSqHAAECBAgQIECAQAsFBO4txFU0AQIECBAgQIAAgWYJCNybJakcAgQIECBAgAABAi0UELi3EFfRBAgQIECAAAECBJolIHBvlqRyCBAgQIAAAQIECLRQQODeQlxFEyBAgAABAgQIEGiWwP8PqeKduygnhKMAAAAASUVORK5CYII=" width="60%" style="display: block; margin: auto;" /></p>
<p>Again we see that the negative binomial model does a much better job
predicting the proportion of observed zeros than the Poisson.</p>
<p>However, if we look instead at the distribution of the maximum value
in the replications, we can see that the Poisson model makes more
realistic predictions than the negative binomial:</p>
<div class="sourceCode" id="cb25"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb25-1"><a href="#cb25-1" tabindex="-1"></a><span class="fu">ppc_stat</span>(y, yrep_poisson, <span class="at">stat =</span> <span class="st">&quot;max&quot;</span>)</span></code></pre></div>
<p><img role="img" src="data:image/png;base64,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" width="60%" style="display: block; margin: auto;" /></p>
<div class="sourceCode" id="cb26"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb26-1"><a href="#cb26-1" tabindex="-1"></a><span class="fu">ppc_stat</span>(y, yrep_nb, <span class="at">stat =</span> <span class="st">&quot;max&quot;</span>)</span></code></pre></div>
<p><img role="img" src="data:image/png;base64,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" width="60%" style="display: block; margin: auto;" /></p>
<div class="sourceCode" id="cb27"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb27-1"><a href="#cb27-1" tabindex="-1"></a><span class="fu">ppc_stat</span>(y, yrep_nb, <span class="at">stat =</span> <span class="st">&quot;max&quot;</span>, <span class="at">binwidth =</span> <span class="dv">100</span>) <span class="sc">+</span> </span>
<span id="cb27-2"><a href="#cb27-2" tabindex="-1"></a>  <span class="fu">coord_cartesian</span>(<span class="at">xlim =</span> <span class="fu">c</span>(<span class="sc">-</span><span class="dv">1</span>, <span class="dv">5000</span>))</span></code></pre></div>
<p><img role="img" src="data:image/png;base64,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" width="60%" style="display: block; margin: auto;" /></p>
<p>See Figure 7 in <a href="#gabry2019">Gabry et al. (2019)</a> for
another example of using <code>ppc_stat</code>.</p>
<p><br></p>
</div>
</div>
<div id="other-ppcs-and-ppcs-by-group" class="section level2">
<h2>Other PPCs and PPCs by group</h2>
<p>There are many additional PPCs available, including plots of
predictive intervals, distributions of predictive errors, and more. For
links to the documentation for all of the various PPC plots see
<code>help(&quot;PPC-overview&quot;)</code> from R or the <a href="https://mc-stan.org/bayesplot/reference/index.html#section-ppc">online
documentation</a> on the Stan website.</p>
<p>The <code>available_ppc</code> function can also be used to list the
names of all PPC plotting functions:</p>
<div class="sourceCode" id="cb28"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb28-1"><a href="#cb28-1" tabindex="-1"></a><span class="fu">available_ppc</span>()</span></code></pre></div>
<pre><code>bayesplot PPC module:
  ppc_bars
  ppc_bars_grouped
  ppc_boxplot
  ppc_dens
  ppc_dens_overlay
  ppc_dens_overlay_grouped
  ppc_dots
  ppc_ecdf_overlay
  ppc_ecdf_overlay_grouped
  ppc_error_binned
  ppc_error_hist
  ppc_error_hist_grouped
  ppc_error_scatter
  ppc_error_scatter_avg
  ppc_error_scatter_avg_grouped
  ppc_error_scatter_avg_vs_x
  ppc_freqpoly
  ppc_freqpoly_grouped
  ppc_hist
  ppc_intervals
  ppc_intervals_grouped
  ppc_km_overlay
  ppc_km_overlay_grouped
  ppc_loo_intervals
  ppc_loo_pit_ecdf
  ppc_loo_pit_overlay
  ppc_loo_pit_qq
  ppc_loo_ribbon
  ppc_pit_ecdf
  ppc_pit_ecdf_grouped
  ppc_ribbon
  ppc_ribbon_grouped
  ppc_rootogram
  ppc_scatter
  ppc_scatter_avg
  ppc_scatter_avg_grouped
  ppc_stat
  ppc_stat_2d
  ppc_stat_freqpoly
  ppc_stat_freqpoly_grouped
  ppc_stat_grouped
  ppc_violin_grouped</code></pre>
<p>Many of the available PPCs can also be carried out within levels of a
grouping variable. Any function for PPCs by group will have a name
ending in <code>_grouped</code> and will accept an additional argument
<code>group</code>. The full list of currently available
<code>_grouped</code> functions is:</p>
<div class="sourceCode" id="cb30"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb30-1"><a href="#cb30-1" tabindex="-1"></a><span class="fu">available_ppc</span>(<span class="at">pattern =</span> <span class="st">&quot;_grouped&quot;</span>)</span></code></pre></div>
<pre><code>bayesplot PPC module:
(matching pattern &#39;_grouped&#39;) 
  ppc_bars_grouped
  ppc_dens_overlay_grouped
  ppc_ecdf_overlay_grouped
  ppc_error_hist_grouped
  ppc_error_scatter_avg_grouped
  ppc_freqpoly_grouped
  ppc_intervals_grouped
  ppc_km_overlay_grouped
  ppc_pit_ecdf_grouped
  ppc_ribbon_grouped
  ppc_scatter_avg_grouped
  ppc_stat_freqpoly_grouped
  ppc_stat_grouped
  ppc_violin_grouped</code></pre>
<div id="ppc_stat_grouped" class="section level4">
<h4>ppc_stat_grouped</h4>
<p>For example, <code>ppc_stat_grouped</code> is the same as
<code>ppc_stat</code> except that the test statistic is computed within
levels of the grouping variable and a separate plot is made for each
level:</p>
<div class="sourceCode" id="cb32"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb32-1"><a href="#cb32-1" tabindex="-1"></a><span class="fu">ppc_stat_grouped</span>(y, yrep_nb, <span class="at">group =</span> roaches<span class="sc">$</span>treatment, <span class="at">stat =</span> <span class="st">&quot;prop_zero&quot;</span>)</span></code></pre></div>
<p><img role="img" src="data:image/png;base64,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" width="60%" style="display: block; margin: auto;" /></p>
<p>See Figure 8 in <a href="#gabry2019">Gabry et al. (2019)</a> for
another example of using <code>ppc_stat_grouped</code>.</p>
<p><br></p>
</div>
</div>
<div id="providing-an-interface-to-bayesplot-ppcs-from-another-package" class="section level2">
<h2>Providing an interface to bayesplot PPCs from another package</h2>
<p>The <strong>bayesplot</strong> package provides the S3 generic
function <code>pp_check</code>. Authors of R packages for Bayesian
inference are encouraged to define methods for the fitted model objects
created by their packages. This will hopefully be convenient for both
users and developers and contribute to the use of the same naming
conventions across many of the R packages for Bayesian data
analysis.</p>
<p>To provide an interface to <strong>bayesplot</strong> from your
package, you can very easily define a <code>pp_check</code> method (or
multiple <code>pp_check</code> methods) for the fitted model objects
created by your package. All a <code>pp_check</code> method needs to do
is provide the <code>y</code> vector and <code>yrep</code> matrix
arguments to the various plotting functions included in
<strong>bayesplot</strong>.</p>
<div id="defining-a-pp_check-method" class="section level3">
<h3>Defining a <code>pp_check</code> method</h3>
<p>Here is an example for how to define a simple <code>pp_check</code>
method in a package that creates fitted model objects of class
<code>&quot;foo&quot;</code>. We will define a method <code>pp_check.foo</code>
that extracts the data <code>y</code> and the draws from the posterior
predictive distribution <code>yrep</code> from an object of class
<code>&quot;foo&quot;</code> and then calls one of the plotting functions from
<strong>bayesplot</strong>.</p>
<p>Suppose that objects of class <code>&quot;foo&quot;</code> are lists with named
components, two of which are <code>y</code> and <code>yrep</code>.
Here’s a simple method <code>pp_check.foo</code> that offers the user
the option of two different plots:</p>
<div class="sourceCode" id="cb33"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb33-1"><a href="#cb33-1" tabindex="-1"></a><span class="co"># @param object An object of class &quot;foo&quot;.</span></span>
<span id="cb33-2"><a href="#cb33-2" tabindex="-1"></a><span class="co"># @param type The type of plot.</span></span>
<span id="cb33-3"><a href="#cb33-3" tabindex="-1"></a><span class="co"># @param ... Optional arguments passed on to the bayesplot plotting function.</span></span>
<span id="cb33-4"><a href="#cb33-4" tabindex="-1"></a>pp_check.foo <span class="ot">&lt;-</span> <span class="cf">function</span>(object, <span class="at">type =</span> <span class="fu">c</span>(<span class="st">&quot;multiple&quot;</span>, <span class="st">&quot;overlaid&quot;</span>), ...) {</span>
<span id="cb33-5"><a href="#cb33-5" tabindex="-1"></a>  type <span class="ot">&lt;-</span> <span class="fu">match.arg</span>(type)</span>
<span id="cb33-6"><a href="#cb33-6" tabindex="-1"></a>  y <span class="ot">&lt;-</span> object[[<span class="st">&quot;y&quot;</span>]]</span>
<span id="cb33-7"><a href="#cb33-7" tabindex="-1"></a>  yrep <span class="ot">&lt;-</span> object[[<span class="st">&quot;yrep&quot;</span>]]</span>
<span id="cb33-8"><a href="#cb33-8" tabindex="-1"></a>  <span class="fu">stopifnot</span>(<span class="fu">nrow</span>(yrep) <span class="sc">&gt;=</span> <span class="dv">50</span>)</span>
<span id="cb33-9"><a href="#cb33-9" tabindex="-1"></a>  samp <span class="ot">&lt;-</span> <span class="fu">sample</span>(<span class="fu">nrow</span>(yrep), <span class="at">size =</span> <span class="fu">ifelse</span>(type <span class="sc">==</span> <span class="st">&quot;overlaid&quot;</span>, <span class="dv">50</span>, <span class="dv">5</span>))</span>
<span id="cb33-10"><a href="#cb33-10" tabindex="-1"></a>  yrep <span class="ot">&lt;-</span> yrep[samp, ]</span>
<span id="cb33-11"><a href="#cb33-11" tabindex="-1"></a>  </span>
<span id="cb33-12"><a href="#cb33-12" tabindex="-1"></a>  <span class="cf">if</span> (type <span class="sc">==</span> <span class="st">&quot;overlaid&quot;</span>) {</span>
<span id="cb33-13"><a href="#cb33-13" tabindex="-1"></a>    <span class="fu">ppc_dens_overlay</span>(y, yrep, ...) </span>
<span id="cb33-14"><a href="#cb33-14" tabindex="-1"></a>  } <span class="cf">else</span> {</span>
<span id="cb33-15"><a href="#cb33-15" tabindex="-1"></a>    <span class="fu">ppc_hist</span>(y, yrep, ...)</span>
<span id="cb33-16"><a href="#cb33-16" tabindex="-1"></a>  }</span>
<span id="cb33-17"><a href="#cb33-17" tabindex="-1"></a>}</span></code></pre></div>
<p>To try out <code>pp_check.foo</code> we can just make a list with
<code>y</code> and <code>yrep</code> components and give it class
<code>foo</code>:</p>
<div class="sourceCode" id="cb34"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb34-1"><a href="#cb34-1" tabindex="-1"></a>x <span class="ot">&lt;-</span> <span class="fu">list</span>(<span class="at">y =</span> <span class="fu">rnorm</span>(<span class="dv">200</span>), <span class="at">yrep =</span> <span class="fu">matrix</span>(<span class="fu">rnorm</span>(<span class="fl">1e5</span>), <span class="at">nrow =</span> <span class="dv">500</span>, <span class="at">ncol =</span> <span class="dv">200</span>))</span>
<span id="cb34-2"><a href="#cb34-2" tabindex="-1"></a><span class="fu">class</span>(x) <span class="ot">&lt;-</span> <span class="st">&quot;foo&quot;</span></span></code></pre></div>
<div class="sourceCode" id="cb35"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb35-1"><a href="#cb35-1" tabindex="-1"></a><span class="fu">color_scheme_set</span>(<span class="st">&quot;purple&quot;</span>)</span>
<span id="cb35-2"><a href="#cb35-2" tabindex="-1"></a><span class="fu">pp_check</span>(x, <span class="at">type =</span> <span class="st">&quot;multiple&quot;</span>, <span class="at">binwidth =</span> <span class="fl">0.3</span>)</span></code></pre></div>
<p><img role="img" src="data:image/png;base64,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" width="60%" style="display: block; margin: auto;" /></p>
<div class="sourceCode" id="cb36"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb36-1"><a href="#cb36-1" tabindex="-1"></a><span class="fu">color_scheme_set</span>(<span class="st">&quot;darkgray&quot;</span>)</span>
<span id="cb36-2"><a href="#cb36-2" tabindex="-1"></a><span class="fu">pp_check</span>(x, <span class="at">type =</span> <span class="st">&quot;overlaid&quot;</span>)</span></code></pre></div>
<p><img role="img" src="data:image/png;base64,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" width="60%" style="display: block; margin: auto;" /></p>
</div>
<div id="examples-of-pp_check-methods-in-other-packages" class="section level3">
<h3>Examples of <code>pp_check</code> methods in other packages</h3>
<p>Several packages currently use this approach to provide an interface
to <strong>bayesplot</strong>’s graphical posterior predictive checks.
See, for example, the <code>pp_check</code> methods in the <a href="https://CRAN.R-project.org/package=rstanarm"><strong>rstanarm</strong></a>
and <a href="https://CRAN.R-project.org/package=brms"><strong>brms</strong></a>
packages.</p>
<p><br></p>
</div>
</div>
<div id="references" class="section level2">
<h2>References</h2>
<p>Buerkner, P. (2017). brms: Bayesian Regression Models using Stan. R
package version 1.7.0. <a href="https://CRAN.R-project.org/package=brms" class="uri">https://CRAN.R-project.org/package=brms</a></p>
<p>Gabry, J., and Goodrich, B. (2017). rstanarm: Bayesian Applied
Regression Modeling via Stan. R package version 2.15.3. <a href="https://mc-stan.org/rstanarm/" class="uri">https://mc-stan.org/rstanarm/</a>, <a href="https://CRAN.R-project.org/package=rstanarm" class="uri">https://CRAN.R-project.org/package=rstanarm</a></p>
<p>Gabry, J. , Simpson, D. , Vehtari, A. , Betancourt, M. and Gelman, A.
(2019), Visualization in Bayesian workflow. <em>J. R. Stat. Soc. A</em>,
182: 389-402. :10.1111/rssa.12378. (<a href="https://rss.onlinelibrary.wiley.com/doi/full/10.1111/rssa.12378">journal
version</a>, <a href="https://arxiv.org/abs/1709.01449">arXiv
preprint</a>, <a href="https://github.com/jgabry/bayes-vis-paper">code
on GitHub</a>) <a id="gabry2019"></a></p>
<p>Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A.,
and Rubin, D. B. (2013). <em>Bayesian Data Analysis</em>. Chapman &amp;
Hall/CRC Press, London, third edition.</p>
<p>Stan Development Team. <em>Stan Modeling Language Users Guide and
Reference Manual</em>. <a href="https://mc-stan.org/users/documentation/" class="uri">https://mc-stan.org/users/documentation/</a></p>
</div>



<!-- code folding -->


<!-- dynamically load mathjax for compatibility with self-contained -->
<script>
  (function () {
    var script = document.createElement("script");
    script.type = "text/javascript";
    script.src  = "https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML";
    document.getElementsByTagName("head")[0].appendChild(script);
  })();
</script>

</body>
</html>